Evaluation of Superficial and Deep Specimens for Isolation and Identifi-cation of Bacterial Isolates from Diabetic Foot Infections.
Bibliographic record
Abstract
Worldwide, Diabetic foot infections (DFIs) are a major medical, social, and economic problem reaching epidemic proportions carrying the increased risk of complications.[1,2] About 25% of the diabetics have the risk of developing foot ulceration which is one of the leading cause of mortality and morbidity in developing coun-tries.2,3,4The most feared complication of in-fected diabetic foot ulcers is gangrene which results in amputations and occurs 10-30 times more often in diabetics. About one major am-putation in 30 seconds worldwide in diabetics and the elevated mortality at follow up, rang-ing from 13% to 40% at 1 year to 39% – 80% at 5 years requires urgent strategies towards prevention of foot ulceration and amputations.[3,5] Once the protective layer of skin is bro-ken, the deep tissues are exposed to bacterial infection that progresses rapidly.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".