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• W2209423009 abstract "1When an investigation is designed, it is helpful to have a theoretical model of the problem. A. In this study, the authors chose to look at the relationship of procalcitonin and pneumonia. What is already known about procalcitonin levels and infection? Can the procalcitonin level differentiate between viral and bacterial disease? Is procalcitonin level correlated with outcome in bacterial illness? B. Create a schematic conceptual model that shows how procalcitonin and Pneumonia Severity Index (PSI) are related to pneumonia outcomes. What are the likely shapes of these relationships? Procalcitonin level can be treated as a continuous variable, can be divided—as these authors do—into several categories, or can be treated as a binary (low, high) variable. What are the advantages and disadvantages of each approach? According to your model, do you expect progressively higher procalcitonin levels to correlate with progressively worse outcomes or do you expect normal procalcitonin levels to predict good outcomes and abnormal values to have similar frequencies of adverse outcome regardless of the magnitude of the elevation?2 What are the advantages and disadvantages of choosing all-cause death at 30 days as the primary outcome of interest? When emergency physicians determine the disposition (home, regular bed, monitored bed, ICU) of pneumonia patients, are they thinking about 30-day mortality or something else? What other outcomes might be of interest? What assumptions must be made about hospital admission to justify the use of 30-day mortality as an outcome in this study? Are these assumptions likely to be correct?3 The authors use likelihood ratios to describe their results (their Table 3). What is a positive likelihood ratio (LR+)? A negative likelihood ratio (LR−)? Contrast likelihood ratios to other measures of test performance and describe their advantages and disadvantages. Why, in theory at least, are likelihood ratios particularly useful at the bedside? What does the LR− of 0.09 (95% confidence interval [CI] 0.02 to 0.36) found in this study mean quantitatively and qualitatively? What numbers is it based on (calculate it!)?4 Multicenter studies make it possible to enroll large numbers of subjects and offer a greater chance for external generalizability but present analytic challenges. What are some of the analytic challenges that arise from multicenter studies and what are some of the techniques used to overcome these? Consider issues about the presentation of results and the statistical analysis of the data. In this study, what information about the role of individual study sites would help readers understand and interpret the meaning of the results?5 What would you choose as the next step in evaluating the effect of procalcitonin testing on pneumonia patients? Should we start using it in clinical practice and see how we like it? Should we test it in an external validation set (how is this done)? Should we conduct a randomized controlled trial? How would you design such a trial? What would the intervention be? What would the outcome of interest be? Q1.a In this study, the authors chose to look at the relationship of procalcitonin and pneumonia. What is already known about procalcitonin levels and infection? Can the procalcitonin level differentiate between viral and bacterial disease? Is procalcitonin level correlated with outcome in bacterial illness? Theoretical model building is the process wherein researchers begin to synthesize information from the medical literature, observations from their own laboratories, and even their own hunches into a coherent and simplified explanation of a complicated phenomenon. The relationships of relevant variables are described in terms of magnitude and direction. These models can be specified graphically, mathematically, or by narrative. Once established, the model helps researchers plan investigations that will test the relationships predicted by the model against observable data. If the data refute the model or part of the model, the model is adjusted or abandoned. Although this process (observe, build theory, test, and repeat) is an essential part of many scientific disciplines, the medical literature has been largely devoid of specific discussions of model building. The absence of discussion of theoretical models can leave readers confused about why the investigators chose to examine a certain variable compared with another seemingly equivalent choice.2Schriger D.L. Suggestions for improving the reporting of clinical research: the role of narrative.Ann Emerg Med. 2005; 45: 437-443Abstract Full Text Full Text PDF PubMed Scopus (30) Google Scholar Let's consider what we know about procalcitonin (based solely on the Huang et al “Introduction” section) and build a theoretical model that will relate procalcitonin levels to the study's primary outcome, 30-day all-cause mortality. What do we know?1)Procalcitonin is increased in bacterial infections but low in viral infections.3Christ-Crain M. Muller B. Procalcitonin in bacterial infections—hype, hope, more or less?.Swiss Med Wkly. 2005; 135: 451-460PubMed Google Scholar2)Procalcitonin has good discrimination for bacterial infections.4Luzzani A. Polati E. Dorizzi R. et al.Comparison of procalcitonin and C-reactive protein as markers of sepsis.Crit Care Med. 2003; 31: 1737-1741Crossref PubMed Scopus (318) Google Scholar, 5Casado-Flores J. Blanco-Quiros A. Asensio J. et al.Serum procalcitonin in children with suspected sepsis: a comparison with C-reactive protein and neutrophil count.Pediatr Crit Care Med. 2003; 4: 190-195Crossref PubMed Scopus (85) Google Scholar, 6Caterino J.M. Scheatzle M.D. Forbes M.L. et al.Bacteremic elder emergency department patients: procalcitonin and white count.Acad Emerg Med. 2004; 11: 393-396PubMed Google Scholar, 7Uzzan B. Cohen R. Nicolas P. et al.Procalcitonin as a diagnostic test for sepsis in critically ill adults and after surgery or trauma: a systematic review and meta-analysis.Crit Care Med. 2006; 34: 1996-2003Crossref PubMed Scopus (612) Google Scholar3)Three trials used low procalcitonin levels to withhold antibiotics in emergency department (ED) patients presenting with respiratory symptoms.8Christ-Crain M. Stolz D. Bingisser R. et al.Procalcitonin-guidance of antibiotic therapy in community-acquired pneumonia: a randomized trial.Am J Respir Crit Care Med. 2006; 174: 84-93Crossref PubMed Scopus (758) Google Scholar, 9Stolz D. Christ-Crain M. Bingisser R. et al.Antibiotic treatment of exacerbations of COPD: a randomized, controlled trial comparing procalcitonin-guidance with standard therapy.Chest. 2007; 131: 9-19Crossref PubMed Scopus (487) Google Scholar, 10Christ-Crain M. Jaccard-Stolz D. Bingisser R. et al.Effect of procalcitonin-guided treatment on antibiotic use and outcome in lower respiratory tract infections: cluster-randomised, single-blinded intervention trial.Lancet. 2004; 363: 600-607Abstract Full Text Full Text PDF PubMed Scopus (925) Google Scholar4)Two meta-analyses concluded that procalcitonin could not differentiate sepsis from noninfectious inflammation in critically ill patients, and procalcitonin had only moderate diagnostic performance at identifying bacteremia in ED patients.11Tang B.M. Eslick G.D. Craig J.C. et al.Accuracy of procalcitonin for sepsis diagnosis in critically ill patients: systematic review and meta-analysis.Lancet Infect Dis. 2007; 7: 210-217Abstract Full Text Full Text PDF PubMed Scopus (711) Google Scholar, 12Jones A.E. Fiechtl J.F. Brown M.D. et al.Procalcitonin test in the diagnosis of bacteremia: a meta-analysis.Ann Emerg Med. 2007; 50: 34-41Abstract Full Text Full Text PDF PubMed Scopus (183) Google Scholar5)Higher procalcitonin scores have been observed to associate with higher Pneumonia Severity Index (PSI) scores in one study but not in another.13Masia M. Gutierrez F. Shum C. et al.Usefulness of procalcitonin levels in community-acquired pneumonia according to the Patients Outcome Research Team Pneumonia Severity Index.Chest. 2005; 128: 2223-2229Crossref PubMed Scopus (156) Google Scholar, 14Beovic B. Kreft S. Osredkar J. et al.Serum procalcitonin levels in patients with mild community-acquired pneumonia.Clin Microbiol Infect. 2005; 11: 1050-1051Crossref PubMed Scopus (11) Google Scholar The first 3 points suggest that procalcitonin may be increased in bacterial infections but not in viral infections. The fifth point demonstrates that higher PSI has not been consistently associated with higher procalcitonin level. Q1.b Create a schematic conceptual model that shows how procalcitonin and Pneumonia Severity Index are related to pneumonia outcomes. What are the likely shapes of these relationships? Procalcitonin level can be treated as a continuous variable, can be divided-as these authors do-into several categories, or can be treated as a binary (low, high) variable. What are the advantages and disadvantages of each approach? According to your model, do you expect progressively higher procalcitonin levels to correlate with progressively worse outcomes or do you expect normal procalcitonin levels to predict good outcomes and abnormal values to have similar frequencies of adverse outcome regardless of the magnitude of the elevation? At this point, it would be appropriate to assert the model's general form. Here is a list of variables that we believe are related to the phenomena of interest. In this case:•There is a relationship between procalcitonin and severe bacterial infections.•There is a relationship between PSI score and procalcitonin level.•There is a relationship between procalcitonin and death. It may be necessary or helpful to specify the causal pathways involved in the model. For example, pneumonia results in endotoxin production, which induces an inflammatory response in the host. This inflammatory response can lead to coagulopathy, vital sign disturbances, organ dysfunction, and death. The intensity of this process is mediated by patient characteristics such as age, sex, and previous health status (comorbidities). Indicators of the inflammatory response include procalcitonin and WBC count. Though procalcitonin does not directly kill the host, it may serve as a marker for the severity of the inflammatory response. The components of PSI score include elements that mediate the illness (eg, age, comorbidities), indirect markers of inflammation, and direct markers of end organ damage (eg, systolic blood pressure, blood urea nitrogen). Typically, the general scheme relating all relevant elements is specified in a diagram. Such diagrams can be informal (Figure 1) or can follow the conventions used for causal diagrams.2Schriger D.L. Suggestions for improving the reporting of clinical research: the role of narrative.Ann Emerg Med. 2005; 45: 437-443Abstract Full Text Full Text PDF PubMed Scopus (30) Google Scholar, 15Greenland S. Pearl J. Robins J.M. Causal diagrams for epidemiologic research.Epidemiology. 1999; 10: 37-48Crossref PubMed Scopus (2637) Google Scholar Once the general relationships among variables are mapped out, the nature of the relationships among sets of variables can be specified. Is procalcitonin thought to be linearly related to PSI score? How exactly is procalcitonin thought to relate to all-cause mortality? Is this relationship linear? Logistic? J-shaped? Over what range of values is each of these specified relationships maintained? It is likewise important to consider the interaction among variables thought to influence the outcome of interest. For example, PSI is related to all-cause mortality and procalcitonin is related to all-cause mortality. When viewed together, how are PSI and procalcitonin jointly thought to relate to all-cause mortality? Does procalcitonin level have the same predictive effect on mortality across all values of PSI? It is of great importance that variables not included in the model be addressed because omitting a variable from a model is equivalent to saying, “I am 100% certain that this variable has no direct or indirect effects on outcome.” Why was the number of lung lobes affected by the pneumonia not included? Surely this could influence mortality. The authors should discuss the rationale for excluding seemingly important variables from the model. Finally, each variable in the model must be specified and operationalized. Is age to be treated as a continuous variable? Is a change in age from 18 to 28 years to be considered equivalent to a change in age from 60 to 70 years? Should procalcitonin be treated as a continuous variable associated with the ordinal variable PSI? Should procalcitonin be converted to an ordinal variable and compared with the ordinal PSI class? At each step of the model generation, from form to specification, the rationale for choices should be described. Ideally, previous theory and data should dictate model generation, but some inductive process is common and should be explained and justified. Once the theoretic model is laid down, specific hypothesis generation is appropriate. In this example, there are many ways one could construct this model because we have so little information to deduce it from. Depending on the form and specifications one chose, a great number of questions could arise. The authors do not explicitly detail their model but hypothesize that “an early singular procalcitonin measurement would aid risk assessment beyond that available from the PSI.” According to the model we constructed, we might have hypothesized “that normal procalcitonin levels will predict cases that are later proved to not be pneumonia. Further, because these patients do not have pneumonia, we hypothesize that their 30-day mortality will be much lower than those proven to have pneumonia.” An alternative hypothesis (based on point 5) could be that “higher procalcitonin scores may associate with higher PSI or CURB 65 scores and, by extension, mortality.” The authors could have dichotomized the procalcitonin results into normal and abnormal a priori. Instead, they choose to divide procalcitonin into tiers. Why? For starters, this follows the lead of 3 previous studies. But this alone is a poor rationale because none of the previous studies provide justification for this choice. A more cogent reason for choosing a tiered approach is to attempt to demonstrate a dose-response relationship between procalcitonin level and mortality. In general, the presence of such a relationship makes it more likely that there is a biological link between the variables, eg, the processes that increase procalcitonin and the processes that lead to death. For example, the more one smokes, the higher the risk of developing lung cancer. Given the author's decision to maintain the tiers, what do you suppose the mortality should be for patients in tier 1 compared with tiers 2, 3, and 4? Indeed tier 1 has a low mortality, at 1.5% (Huang et al, Table 2). However, the dose-response relationship is quickly lost. Tiers 2, 3, and 4 have mortalities of 8.4%, 9.5%, and 8.9%, respectively. The dose-response relationship is further undermined by the Huang et al figures (Figures 2 and 3) that show, again, low mortality for tier 1 procalcitonin level but no relationship between mortality and the higher procalcitonin tiers. Given the authors' decision to divide the procalcitonin into tiers, it is somewhat surprising that the absence of a dose-response relationship is not emphasized. Rather, the procalcitonin levels are repackaged as normal and abnormal. The newly dichotomized procalcitonin level is then analyzed within each of the 5 PSI class subgroups. The authors then claim that the procalcitonin level is a useful adjunct in PSI classes IV and V. This relationship does not appear to have been hypothesized a priori, and we are left to wonder whether the authors, having observed Figures 2 and 3, developed a post hoc theory that normal procalcitonin levels predict good outcomes in patients with high PSI class. If this is the case, such findings must be viewed as hypothesis generating, and no conclusions about their validity should be drawn. Much has been written about the dangers of post hoc subgroup analysis,16Lagakos S.W. The challenge of subgroup analyses—reporting without distorting.N Engl J Med. 2006; 354: 1667-1669Crossref PubMed Scopus (416) Google Scholar, 17Assmann S.F. Pocock S.J. Enos L.E. et al.Subgroup analysis and other (mis)uses of baseline data in clinical trials.Lancet. 2000; 355: 1064-1069Abstract Full Text Full Text PDF PubMed Scopus (884) Google Scholar and this topic will be considered in detail in a future journal club. Consider that instead of emphasizing the results observed in PSI subgroups 4 and 5, the authors could have emphasized the relationship of procalcitonin to mortality in PSI classes I to III. For these PSI classes, the positive likelihood ratio (LR+) is 0.97, suggesting that a high (or positive) procalcitonin level actually reduces the odds of an adverse outcome (see question 3 answer for a more detailed discussion of likelihood ratios). This is dramatically contrary to the stated hypothesis that “an early singular procalcitonin measurement would aid risk assessment beyond that available from the PSI.” Why, then, do the authors believe the procalcitonin level is of clinical value? A detailed theoretical model specifying the manner in which dichotomized procalcitonin level was thought to supersede the PSI score in class IV and V cases but not in I to III cases might allow for more enthusiastic acceptance of the study results. In the absence of this model, the reader is left wondering whether the curious observation that a normal procalcitonin level is associated with good outcomes despite PSI score is a tested truth or random noise. Q2. What are the advantages and disadvantages of choosing all-cause death at 30 days as the primary outcome of interest? When emergency physicians determine the disposition (home, regular bed, monitored bed, ICU) of pneumonia patients, are they thinking about 30-day mortality or something else? What other outcomes might be of interest? What assumptions must be made about hospital admission to justify the use of 30-day mortality as an outcome in this study? Are these assumptions likely to be correct? The selection of outcomes is a critical and necessarily difficult tradeoff in most clinical studies. Researchers must balance the ease of measurement of a particular outcome against the clinical meaning of that outcome. Much has been written about the importance of patient-centered outcomes in clinical research (eg, an asthma study in children should measure days missed from school or patient symptom self-report, not change in forced expiratory volume in one second [FEV-1]), and such outcomes are preferred even if they are harder to measure. In the Huang et al study, the researchers chose all-cause 30-day mortality as the outcome of interest. This outcome is intuitively appealing; it is easy to measure and should have high reliability and validity; it is unlikely there will be much interrater disagreement about whether a person is alive or not! Further, it is clinically obvious that we want to keep people alive, so mortality often seems to be the most important outcome measure possible. Certainly in a randomized clinical trial of different antibiotic agents for pneumonia patients, mortality would be a relevant outcome variable, and 30-day mortality is preferred over shorter-term measures (3-day or 7-day mortality) because it has greater clinical meaning. But is 30-day all-cause mortality the best primary outcome measure for the Huang et al study? Do we truly believe that an initial procalcitonin level obtained in the ED will affect 30-day all-cause mortality? The causal chain would run thus: early knowledge of the procalcitonin level leads to a change in initial management that affects all-cause 30-day mortality. But does our ED treatment of pneumonia have that much influence on 30-day all-cause mortality? Would not the many patients for whom pneumonia is the penultimate stop on the way to a death from other causes obscure any true benefit? Will the outcome of a patient with advanced cancer who develops a mild pneumonia, is treated with antibiotics, improves, but dies of pulmonary embolism 2 weeks later be affected by the measurement of procalcitonin in the ED? Would it be better to shorten the timing of the outcome or change the outcome from all-cause mortality to death from pneumonia? Surely if we cannot observe a change in 3- or 7-day mortality from pneumonia, then it is unlikely that 30-day all-cause mortality will be affected. Conversely, if we did observe a change in a shorter-term, pneumonia-specific outcome, then we might go out to 30 days to determine whether the difference in mortality is sustained. In other words, are a sufficient number of deaths averted at 30 days to make the measurement of procalcitonin worthwhile? Although it might be more telling to determine whether PSI, CURB-65, or procalcitonin was able to predict pneumonia-specific complications and death, such outcomes are more difficult to measure than all-cause mortality because an expert must stand in judgment and determine whether patients' deaths were a result of the pneumonia. Whatever process this expert used will be much more subjective than simply counting the bodies. A second expert would likely be required to judge each case, and the agreement between the judges would then be determined. A third judge may be required in cases of disagreement, and even if there is agreement between the judges, reviewers and critics will be suspicious of the validity of the methods used to determine death causes. So we see here the tradeoff between clinical importance and feasibility. The authors present data demonstrating that increasing CURB 65 and PSI predicts increasing risk of death at 30 days, as does procalcitonin level (as displayed in Huang et al's Table 2 on page 52). But how should clinicians use this knowledge? The implication has been plainly put forth by the PSI developers: Patients with PSI scores of I and II should be discharged, those with scores of IV or V should be admitted, and those within class III could be either discharged or admitted.18Yealy D.M. Auble T.E. Stone R.A. et al.The Emergency Department Community-Acquired Pneumonia Trial: methodology of a quality improvement intervention.Ann Emerg Med. 2004; 43: 770-782Abstract Full Text Full Text PDF PubMed Scopus (47) Google Scholar For this to be a rational plan, we must assume that the act of hospitalizing a patient somehow independently affects the death rate. Conversely, for us to assume that a low score allows us to discharge a patient home assumes that the sole purpose of hospitalization is to prevent 30-day mortality and that the medical care provided during hospitalization of a patient in class I or II was not the very reason that this lower-risk patient did not die. Consider these typical, though hypothetical, examples: Case 1) An 84-year-old man with advanced prostate cancer presents to the ED with fever and mild, nonproductive cough for several days. He appears chronically ill, has fever to 100.8°F (38.2°C), but is in no respiratory distress. He is at his baseline mental status, which is confused and lethargic. A chest radiograph confirms right-sided lower-lobe pneumonia without effusion. He has a peripherally inserted central catheter (PICC) line, through which he gets intermittent intravenous fluids, and has a home health nurse. This individual has a very high PSI of 114 (class IV); no doubt his risk of death at 30 days from any cause is enormous. Even his risk of pneumonia-specific mortality is very high. However, after a brief conversation with his family and primary care physician, you determine that intravenous antibiotics through the PICC line and oxygen can be safely administered at home and his family is more than capable of monitoring him for clinical deterioration.Case 2) A 24-year-old previously healthy graduate student presents with rigors, shaking chills, and a deep, productive cough for 12 hours. He appears uncomfortable and ill. His respiratory rate is 26 breaths/min. Pulse oximetry is 91%. Chest radiograph confirms a dense R middle-lobe consolidation. His WBC count is 22,000, but other laboratory results are unremarkable. He lives alone in a walk-up third-story apartment, with no close friends or family in the area. His PSI score is 50 (class I). These 2 cases illustrate opposite ends of the spectrum. The elderly patient is unlikely to receive any additional benefit from inpatient hospitalization. Indeed, given our current understanding of medical error and iatrogenic injury, hospitalization is more likely to harm than help this patient. Conversely, the young graduate student has a low PSI score, indicating a low risk for 30-day mortality. Would you consider admitting him? We would! Is he likely to die in 30 days? Of course not. He is young and healthy; he could recover from being hit by a truck at high speed. But might he get worse and return to the ED dehydrated in a more advanced state of sepsis and require a longer convalescence? Possibly. Could a short hospitalization with intravenous antibiotics and close monitoring for clinical deterioration at best alter the course of his disease and at worst make him more comfortable than he would be alone in his apartment? Absolutely. The point of these 2 examples is to reinforce the concept that the need for hospitalization is not necessarily dictated by the probability of death. Given the tremendous bioavailability of oral quinolones and other antibiotics, hospitalization is unlikely to alter the ultimate outcome of pneumonia in many cases. Therefore, efforts that assume that the probability of mortality dictates patient disposition are fundamentally misguided. The ability of a normal procalcitonin level to identify PSI classes IV and V suitable for discharge is further undermined by Table E1 from Huang et al, featured here. Here we see that 50% of patients with PSI class V and low procalcitonin level were in a state of severe sepsis on day 1 (12% of PSI IV cases had severe sepsis on day 1). Clearly, discharging patients with severe sepsis would not be possible.Tabled 1 Open table in a new tab Table 2Enrollment and admission rate by hospital.HospitalEligible, No.Enrolled, No.PSI Classes 1+2, Patients Admitted to Hospital, No.PSI Classes 1+2, Patients Enrolled, No.Patients Admitted/Enrolled (PSI I and II Only), No.12029431350.89230417651511.0031748621290.72421410128380.7451549221240.8865220340.75761299110.8281106814240.5891738228330.85101386719330.581146524958690.84121557122240.92131079620320.631463359120.7515625924300.8016858433430.7717797825420.6018572359461150.40191295713300.432043298130.622110410226380.68222621991.0023494931320.9724424013170.76251608633331.0026148441.002743111.0028797917290.59382023206178550.72 Open table in a new tab Further, to use CURB 65, PSI, or procalcitonin to determine the ability to go home, one must assume that the act of hospitalizing the patient was not the very factor that led to the low observed death rate. We may find that the curious subgroup of patients with normal procalcitonin levels despite being PSI class IV and V are the very patients in whom hospitalization dramatically affected the outcome. Perhaps if one has increased procalcitonin level and PSI V, the proverbial cat is out of the bag—these patients are doomed to die regardless of hospitalization—whereas those with normal procalcitonin levels are the very ones who have a less virulent sepsis phenotype and benefit the most from intense care, hydration, and other resuscitative efforts. In the end, all-cause mortality at 30 days has limited clinical relevance when one is deciding which patients can be safely discharged home, thus limiting the usefulness of the study findings. Q3. The authors use likelihood ratios to describe their results (Table 3). What is a positive likelihood ratio (LR−)? A negative likelihood ratio (LR−)? Contrast likelihood ratios to other measures of test performance and describe their advantages and disadvantages. Why, in theory at least, are likelihood ratios particularly useful at the bedside? What does the negative likelihood ratio of 0.09 (95% confidence interval 0.02 to 0.36) found in this study mean quantitatively and qualitatively? What numbers is it based on (calculate it!)? Sensitivity, specificity, positive predictive value, negative predictive value, LR+, and LR− are the most commonly reported measures of diagnostic test characteristics. These are all summary measures that attempt to reduce the classic 2×2 table (4 numbers) into some smaller number of numbers to increase ease of use. When reporting research, authors should provide the actual numbers of true positives, true negatives, false positives, and false negatives because each of the aforementioned summary measures can be derived from them, whereas the converse is not so. Tabled 1Disease (+)Disease (−)Test (+)True positives (a)False positives (b)Test (−)False negatives (c)True negatives (d) Open table in a new tab Sensitivity, specificity, and positive and negative predictive value have been discussed in a previous journal club.19Barrett T.W. Schriger D.L. Practical considerations in HIV testing in the emergency department, characteristics of diagnostic tests, and the role of sensitivity analysis in observational studies: answers to March 2008 Journal Club questions.Ann Emerg Med. 2008; 52: 170-181Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar The bedside usefulness of sensitivity and specificity has been criticized because these measures desc" @default.
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• W2209423009 title "Risk Prediction With Procalcitonin and Clinical Rules in Community-Acquired Pneumonia" @default.
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