Levels of wound calprotectin and other inflammatory biomarkers aid in deciding which patients with a diabetic foot ulcer need antibiotic therapy (<scp>INDUCE</scp> study)
Bibliographic record
Abstract
AIMS: Deciding if a diabetic foot ulcer is infected in a community setting is challenging without validated point-of-care tests. Four inflammatory biomarkers were investigated to develop a composite algorithm for mildly infected diabetic foot ulcers: venous white cell count, C-reactive protein (CRP) and procalcitonin, and a novel wound exudate calprotectin assay. Calprotectin is a marker of neutrophilic inflammation. METHODS: In a prospective study, people with uninfected or mildly infected diabetic foot ulcers who had not received oral antibiotics in the preceding 2 weeks were recruited from community podiatry clinics for measurement of inflammatory biomarkers. Antibiotic prescribing decisions were based on clinicians' baseline assessments and participants were reviewed 1 week later; ulcer infection was defined by clinicians' overall impression from their two assessments. RESULTS: ) in uninfected ulcers. Baseline CRP for mild infection was 9.00 mg/ml and 6.00 mg/ml for uninfected ulcers; most procalcitonin levels were undetectable. Median calprotectin level in infected diabetic foot ulcers was 1437 ng/ml and 879 ng/ml in uninfected diabetic foot ulcers. Area under the receiver operating characteristic curve for a composite algorithm incorporating calprotectin, CRP, white cell count and ulcer area was 0.68 (95% confidence intervals 0.52-0.82), sensitivity 0.64, specificity 0.81. CONCLUSIONS: A composite algorithm including CRP, calprotectin, white cell count and ulcer area may help to distinguish uninfected from mildly infected diabetic foot ulcers. Venous procalcitonin is unhelpful for mild diabetic foot ulcer infection.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".