Mortality in Patients with Necrotizing Fasciitis
Bibliographic record
Abstract
BACKGROUND: The prognostic factors that determine outcome in patients with necrotizing fasciitis remain poorly understood. The aim of this study was to analyze the variables that affect the mortality and morbidity of patients with necrotizing fasciitis and to create a simple method for estimating the probability of mortality. METHODS: The authors undertook a retrospective review of all patients with necrotizing fasciitis treated in three tertiary care hospitals in Ontario, Canada, between January of 1994 and June of 2001. Demographic, comorbid illness, and disease-specific data were collated and analyzed for associations with outcome. Using logistic regression analysis, probability estimates for the prediction of mortality were developed, based on three contributing independent factors. RESULTS: Ninety-nine patients satisfied the inclusion criteria. Overall mortality was 20 percent. Sixteen patients suffered from amputation or organ loss. The most common comorbidities were diabetes (30 percent), immunocompromised status (17 percent), and chickenpox (11 percent). Advanced age (odds ratio, 1.04; 95 percent confidence interval, 1.01 to 1.08; p = 0.012), streptococcal toxic shock syndrome (odds ratio, 10.54; 95 percent confidence interval, 2.80 to 39.44; p < 0.001), and immunocompromised status (odds ratio, 3.97; 95 percent confidence interval, 1.04 to 15.19; p = 0.044) were independent predictors of mortality and were used to design a formula for the probability of mortality. CONCLUSIONS: Age, streptococcal toxic shock syndrome, and immune status are significant determinants of mortality and can predict the probability of death from necrotizing fasciitis soon after admission. This objective information can guide clinicians in communication with patients and in making clinical decisions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".