Risk factors for nonpurulent leg cellulitis: a systematic review and meta-analysis
Bibliographic record
Abstract
Nonpurulent cellulitis is an acute bacterial infection of the dermal and subdermal tissues that is not associated with purulent drainage, discharge or abscess. The objectives of this systematic review and meta‐analysis were to identify and appraise all controlled observational studies that have examined risk factors for the development of nonpurulent cellulitis of the leg (NPLC). A systematic literature search of electronic databases and grey literature sources was performed in July 2015. The Newcastle–Ottawa Scale (NOS) was used to assess methodological quality of included studies. Of 3059 potentially eligible studies retrieved and screened, six case–control studies were included. An increased risk of developing NPLC was associated with previous cellulitis [odds ratio (OR) 40·3, 95% confidence interval (CI) 22·6–72·0], wound (OR 19·1, 95% CI 9·1–40·0), current leg ulcers (OR 13·7, 95% CI 7·9–23·6), lymphoedema/chronic leg oedema (OR 6·8, 95% CI 3·5–13·3), excoriating skin diseases (OR 4·4, 95% CI 2·7–7·1), tinea pedis (OR 3·2, 95% CI 1·9–5·3) and body mass index > 30 kg m−2 (OR 2·4, 95% CI 1·4–4·0). Diabetes, smoking and alcohol consumption were not associated with NPLC. Although diabetics may have been underrepresented in the included studies, local risk factors appear to play a more significant role in the development of NPLC than do systemic risk factors. Clinicians should consider the treatment of modifiable risk factors including leg oedema, wounds, ulcers, areas of skin breakdown and toe‐web intertrigo while administering antibiotic treatment for NPLC.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.038 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".