Does This Patient Have an Infection of a Chronic Wound?
Bibliographic record
Abstract
CONTEXT: Chronic wounds (those that have not undergone orderly healing) are commonly encountered, but determining whether wounds are infected is often difficult. The current reference standard for the diagnosis of infection of a chronic wound is a deep tissue biopsy culture, which is an invasive procedure. OBJECTIVES: To determine the accuracy of clinical symptoms and signs to diagnose infection in chronic wounds and to determine whether there is a preferred noninvasive method for culturing chronic wounds. DATA SOURCES: We searched multiple databases from inception through November 18, 2011, to identify studies focusing on diagnosis of infection in a chronic wound. STUDY SELECTION: Original studies were selected if they had extractable data describing historical features, symptoms, signs, or laboratory markers or were radiologic studies compared with a reference standard for diagnosing infection in patients with chronic wounds. Of 341 studies initially retrieved, 15 form the basis of this review. These studies include 985 participants with a total of 1056 chronic wounds. The summary prevalence of wound infection was 53%. DATA EXTRACTION: Three authors independently assigned each study a quality grade, using previously published criteria. One author abstracted operating characteristic data. DATA SYNTHESIS: An increase in the level of pain (likelihood ratio range, 11-20) made infection more likely, but its absence (negative likelihood ratio range, 0.64-0.88) did not rule out infection. Other items in the history and physical examination, in isolation or in combination, appeared to have limited utility when infection was diagnosed in chronic wounds. Routine laboratory studies had uncertain value in predicting infection of a chronic wound. CONCLUSIONS: The presence of increasing pain may make infection of a chronic wound more likely. Further evidence is required to determine which, if any, type of quantitative swab culture is most diagnostic.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".