Bibliographic record
Abstract
Foot infections are common in patients with diabetes and are associated with high morbidity and risk of lower extremity amputation. Diabetic foot infections are classified as mild, moderate, or severe. Gram-positive bacteria, such as Staphylococcus aureus and beta-hemolytic streptococci, are the most common pathogens in previously untreated mild and moderate infection. Severe, chronic, or previously treated infections are often polymicrobial. The diagnosis of diabetic foot infection is based on the clinical signs and symptoms of local inflammation. Infected wounds should be cultured after debridement. Tissue specimens obtained by scraping the base of the ulcer with a scalpel or by wound or bone biopsy are strongly preferred to wound swabs. Imaging studies are indicated for suspected deep soft tissue purulent collections or osteomyelitis. Optimal management requires aggressive surgical debridement and wound management, effective antibiotic therapy, and correction of metabolic abnormalities (mainly hyperglycemia and arterial insufficiency). Treatment with antibiotics is not required for noninfected ulcers. Mild soft tissue infection can be treated effectively with oral antibiotics, including dicloxacillin, cephalexin, and clindamycin. Severe soft tissue infection can be initially treated intravenously with ciprofloxacin plus clindamycin; piperacillin/tazobactam; or imipenem/cilastatin. The risk of methicillin-resistant S. aureus infection should be considered when choosing a regimen. Antibiotic treatment should last from one to four weeks for soft tissue infection and six to 12 weeks for osteomyelitis and should be followed by culture-guided definitive therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".