[Skin and osteoarticular infections of the diabetic foot. Role of infection].
Bibliographic record
Abstract
A MAJOR PROBLEM: Two-thirds of all amputations involve infection. Infection is favored by dysfunction of the antibacterial defense systems due to high blood glucose and vascular disorders. DIAGNOSIS: General signs of infection are usually not found. A careful exploration is required to rule out or confirm osteitis in order to guide surgery and plan the antibiotic regimen. A history of chronic and/or recurrent ulceration or direct signs at inspection may be suggestive of osteitis. Radiographic signs are late and nonspecific. Scintigraphy scans are difficult to interpret. Magnetic resonance imaging can be quite helpful in difficult cases. BACTERIOLOGICAL PROOF: Staphylococcus aureus and to a lesser extent streptococci account for almost all of the superficial infections in the diabetic foot. In case of deep ulceration, it is important to obtain deep specimens at surgical cleansing as more superficial samples are easily contaminated. Nevertheless, if Staphylococcus aureus is isolated from pus coming from a deep zone fistulizing to the skin, it is likely the causal agent since 80% of all bone infections involve S. aureus. Other germs besides staphylococci and streptococci include enterobacteria (40%), enterococci (26%) and pseudomonas (7%). Several germs are involved in about 70% of cases with a probable synergetic effect between the different bacterial colonies within the infected tissues.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.014 |
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".