Necrotizing Soft Tissue Infection: Diagnostic Accuracy of Physical Examination, Imaging, and LRINEC Score
Bibliographic record
Abstract
OBJECTIVE: We sought to summarize accuracy of physical examination, imaging, and Laboratory Risk Indicator for Necrotizing Fasciitis (LRINEC) score in diagnosis of necrotizing soft tissue infection (NSTI) in adults with a soft tissue infection clinically concerning for NSTI. SUMMARY OF BACKGROUND DATA: NSTI is a life-threatening diagnosis. Delay to diagnosis and surgical management is associated with increased mortality. METHODS: We searched 6 databases from inception through November 2017. We included English-language studies reporting diagnostic accuracy of testing or LRINEC Score. Outcome was NSTI confirmed by surgery or histopathology. Two reviewers screened all citations and extracted data independently. Summary measures were obtained from the Hierarchical Summary Receiver Operating Characteristic model. RESULTS: From 2,290 citations, we included 23 studies (n = 5982). Of physical examination signs, pooled sensitivity and specificity for fever was 46.0% and 77.0% respectively, for hemorrhagic bullae 25.2% and 95.8%, and for hypotension 21.0% and 97.7%. Computed tomography (CT) had sensitivity of 88.5% and specificity of 93.3%, while plain radiography had sensitivity of 48.9% and specificity of 94.0%. Finally, LRINEC ≥ 6 had sensitivity of 68.2% and specificity of 84.8%, while LRINEC ≥ 8 had sensitivity of 40.8% and specificity of 94.9%. CONCLUSIONS: Absence of any 1 physical examination feature (eg, fever or hypotension) is not sufficient to rule-out NSTI. CT is superior to plain radiography. LRINEC had poor sensitivity, and should not be used to rule-out NSTI. Given the poor sensitivity of these tests, a high clinical suspicion warrants early surgical consultation for definitive diagnosis and management.
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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 | 0.033 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.021 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".