Etiology and Biomechanics of Midfoot (Lisfranc) Injuries in Athletes
Bibliographic record
Abstract
Tarsometatarsal (TMT) dislocations are an uncommon but debilitating athletic injury. When symptomatic midfoot instability persists, an injured athlete frequently requires surgical stabilization and rehabilitation for up to 9 months before returning to full athletic participation. Unfortunately, the limited biomechanical knowledge of this injury prevents prophylactic measures from being developed that could reduce an athlete's risk of injury. The goal of this article is to summarize the literature on TMT dislocations, with a particular emphasis on the relevant biomechanics, in an attempt to clarify the circumstances and mechanisms under which these injuries occur. Since athletic injuries represent only a small portion of all TMT dislocations, other categories of injuries are also considered for the insight they provide. This review first summarizes the anatomy of the TMT joint as well as the clinical details surrounding TMT dislocations. The various hypothesized injury mechanisms are then reviewed with particular attention given to cadaveric studies that investigate these mechanisms. Based on this critical review, gaps in the research related to epidemiologic data, full-scale and component testing, numerical modeling, and countermeasure development, are identified. Only by improving our understanding of the causes and biomechanics can steps be taken to protect athletes from these injuries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".