Effective detection and management of low-velocity Lisfranc injuries in the emergency setting: principles for a subtle and commonly missed entity.
Bibliographic record
Abstract
OBJECTIVE: To improve the ability of primary care physicians to recognize the mechanisms and common presentations of low-velocity Lisfranc injuries (LFIs) and to impart an improved understanding of the role of imaging and principles of primary care in low-velocity LFIs. SOURCES OF INFORMATION: A MEDLINE literature review was performed and the results were summarized, reviewing anatomy and mechanisms, clinical and imaging-based diagnoses, and management principles in the primary care setting. MAIN MESSAGE: Low-velocity LFIs result from various mechanisms and can have very subtle findings on clinical examination and imaging. A high degree of suspicion and caution are warranted when managing this type of injury. CONCLUSION: Although potentially devastating if missed, if a few treatment principles for low-velocity LFIs are applied from the initial presentation onward, outcomes from this injury can be optimized.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".