Predictors of Mortality in Patients With Necrotizing Fasciitis: A Literature Review and Multivariate Analysis
Bibliographic record
Abstract
Background: Necrotizing fasciitis (NF) is a life-threatening infectious disease that can result in significant morbidity and mortality. Previously identified factors have not been verified in a large population. The objective of this study is to further examine the relationship of patient factors in NF mortality. Methods: This study is a retrospective review on patients ≥18 years old diagnosed with NF at the provincial referral centres from 2004 to 2016. The following data were examined: demographics, comorbidities, laboratory values, length of stay, and inhospital mortality. Results: Three hundred forty patients satisfied the inclusion criteria: 297 survived and were discharged, 43 died in hospital. In multivariate analysis, a prognostic model for NF mortality identified age >60 years, elevated creatinine, abnormal blood platelets, and group A β-hemolytic Streptococcus (GABS) infection. Conclusions: Multiple factors were associated with mortality in NF. The strongest univariate association with mortality was age >60 years. In addition, a history of hypertension and/or dyslipidemia, renal disease, and the presence of GABS contributed to a predictive model for inhospital NF mortality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".