Predictors of Fatality in Hemolytic Uremic Syndrome Lack Numbers
Bibliographic record
Abstract
To the Editor.—As an exercise in evidence-based medicine, fellows in the clinical training program at our institution dissect a current article at a monthly journal-club meeting. We recently reviewed the case-control study on predictors of death in hemolytic uremic syndrome by Oakes et al.1 We have the following concerns regarding a conceptual error in the statistical analysis.Oakes et al described the causes of death in children who presented with hemolytic uremic syndrome over a 30-year period in Utah. Using information from a registry of 358 patients, they used a case-control design to determine the effect of prognostic variables, both clinical (bloody diarrhea, oliguria, anuria, fever, seizures, coma, edema, vomiting, dehydration, and lethargy) and laboratory parameters (hematocrit, white blood cell count), on the outcome of interest: death. They used both univariate and multivariate analyses to identify key prognostic variables. They presented the results of the univariate analysis as a “relative risk”; for example, fatal cases were associated with a relative risk of elevated hematocrit of 7.58 (P = .00045). Given the study design, this analysis is backward: the risk of the prognostic factor (elevated hematocrit) is given as a function of the outcome (death)! A more conventional approach for a case-control study, which provides more intuitive and useful information, would be to use an odds ratio, which approximates the relative risk of the outcome (death) given the presence or absence of the prognostic variable (elevated hematocrit). Using the data presented, the odds ratio of death for a patient with an elevated hematocrit is 16.8 (P = .00002, Fisher's exact test), meaning that the odds of dying are ∼17-fold higher in a patient with an elevated hematocrit. We view this as a fundamental, conceptual error, highlighting the importance of a clear distinction between outcome variables and prognostic variables and the utility of the odds ratio in approaching the analysis of a case-control study on prognosis. The error provided an excellent platform for discussion at our educational journal club!Second, the authors used conditional logistic regression to determine which variables were independently associated with death. Only an elevated white blood cell count and an elevated hematocrit were independent predictors, yet the authors' conclusions include oligoanuria and dehydration as important prognostic variables. We feel that the analysis supports only the elevated white blood cell count and the elevated hematocrit as significant prognostic variables.We also noted incidental discrepancies between the text and Table 3 and request clarification from the authors: P value for edema (text: P = .035; table: P = 0.35); percent lethargy among nonfatal cases (text: 49.1%; table: 66.1%); P value for lethargy (text: P = .016; table: P = 0.3); and seizures listed in the table as significant (P < .05) variable but not in text.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".