395 Predictors of Mortality in Patients with Necrotizing Fasciitis: A Multivariate Analysis
Bibliographic record
Abstract
Necrotizing fasciitis (NF) is a life-threatening infectious disease that can result in significant morbidity and mortality. Previous work has identified older age, diabetes mellitus, renal impairment, cardiovascular disease, cirrhosis, low hemoglobin, lower platelets, elevated creatinine, admission to ICU and hospital length of stay. The objective of this study is to further examine the relationship of patient factors in NF mortality. This retrospective review examined patients ≥ 18 years old diagnosed with NF at one of the two regional referral centres from 2004–2016 in one province. The following was examined: demographics, comorbidities, laboratory values and length of stay. 321 patients satisfied the inclusion criteria: 278 survived and were discharged, 43 died in hospital. Using multivariate analysis, age >60, age > 60, elevated creatinine, abnormal platelets and presence of GABS infection were significant predictors for mortality in NF patients. Multiple factors were associated with mortality in NF. The strongest association with mortality in multivariate analysis was age > 60, elevated creatinine, abnormal platelets and presence of GABS infection. Identification of risk factors for mortality in NF may improve treatment of these patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".