Red Flags For Necrotizing Fasciitis: A Case Control Study
Bibliographic record
Abstract
OBJECTIVE: to examine the diagnostic accuracy of traditional 'red flags' for necrotizing fasciitis (NF) on history and physical examination. METHODS: retrospective study of all cases of NF admitted to a large tertiary care hospital between January 1 2004 and December 31 2013, each matched to two control patients with cellulitis. We determined the diagnostic test characteristics of clinical features for distinguishing NF from cellulitis, with emphasis on positive (LR+) and negative (LR-) likelihood ratios. RESULTS: There were no individual findings with sufficient sensitivity to rule out NF (sensitivity ≤ 85% and LR- ≥ 0.5 for all findings). The clinical features that most significantly increased the odds of NF were recent surgery (LR+ 7.0) pain-out-of-proportion (LR+ 4.5), diarrhea (LR+ 6.0), hypotension (LR+ 8.0), altered mental status (LR+ 3.3), erythema progressing beyond margins (LR+3.1), fluctuance (LR+ 5.0), hemorrhagic bullae (LR+ 8.0) and skin necrosis (LR+ 30.0). Each individual finding conferred low sensitivity, but absence of all nine ruled out NF (LR- 0.04). The presence of >=3 findings ruled in NF (LR+ undefined). CONCLUSIONS: When considered together, the traditional 'red flags' for NF may be sufficient to rule in or rule out the diagnosis. If future prospective studies validate these findings, there will be a potential opportunity to expedite NF diagnosis and improve patient outcomes.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".