Diabetic foot infections: how to investigate more efficiently? A retrospective study in a quaternary university center
Bibliographic record
Abstract
BACKGROUND: Diabetic foot infections are frequent and associated with substantial morbidity and substantial cost to the healthcare system. Up to 34% of diabetic patients will develop an ulcer potentially leading to osteomyelitis. Imaging plays a crucial role in the diagnostic process. Imaging modalities to investigate the diabetic foot infection are many and imaging prescription habits remain heterogeneous across physicians. We aimed to improve the appropriateness of imaging examination requested, and performed, for diabetic foot osteomyelitis and we aimed to reduce the overall imaging-related cost. METHODS: Local committee was created to develop an algorithm for suspected diabetic foot osteomyelitis. Best practices were defined by the local algorithm. The algorithm was shared with our physicians. Pre- and post-intervention analysis was conducted retrospectively. All adult diabetic patients with suspected foot osteomyelitis were included. Adherence to best practices was measured. Statistical analysis with Chi-Square and two tailed unpaired t-test was performed. RESULTS: Pre-intervention cohort had 223 patients (mean age: 63; 168 men). Adherence to best practice was 43%. Scintigraphy (48%) preferred over MRI (44%) and performed simultaneously in 15 patients. Post-intervention cohort had 73 patients (mean age: 66; 62 men). Adherence to best practice was 78%, improved by 35% (p < 0.001). MRI (51%) preferred over scintigraphy (23%) and performed simultaneously in three patients. Scintigraphy examinations decreased by 25% (p < 0.001). MRI examinations increased by 7% (p = 0.32). Hospital imaging related fees decreased by 22% per patient (p = 0.002). CONCLUSION: Interval improvement in adequate adherence while reducing unnecessary examinations for patients and decreasing costs for the healthcare system was observed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".