Prise en charge de l’entorse de la tibio-fibulaire inférieure en médecine d’unité : état des lieux et perspectives
Bibliographic record
Abstract
Introduction: ankle injury is one of the most common reasons for consulting in primary care. It represents an important source of physical and operational incapacity in the Army. Among ankle injuries, inferior tibio-fibular ligaments sprain represents a poorly known and underdiagnosed lesion. A wrong diagnosis, resulting in an inadequate treatment, can cause operational incapacity and middle and long-term aftereffects. Method: we realized a 10-point questionnaire with the DELPHI method, bringing together a board of pathology-experts. Different items have been chosen after a literature review. We shared it to all the military doctors and collected 176 complete questionnaires. Results: in average, the rate of correct answers was 55%. The most well-known items were the utility of the Ottawa criteria, the indications of surgery and gravity signs. Meanwhile, inferior tibio-fibular sprain treatment and its middle and long-term complications were weaker spots. Owing a sports medicine qualification was significatively correlated to success in the questionnaire. Conclusion: military doctors’ knowledge of inferior tibio-fibular sprains seems unsatisfying. We suggest delivering a memento-sheet to all military doctors to improve knowledge and patient care in inferior tibio-fibular sprain.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".