Etiology and Biomechanics of Tarsometatarsal Injuries in Professional Football Players
Bibliographic record
Abstract
BACKGROUND: Tarsometatarsal (TMT) dislocations are uncommon yet debilitating athletic injuries, particularly in American football. To date, the mechanisms of athletic TMT dislocation have been described only anecdotally. This lack of information confounds the development of preventative countermeasures. PURPOSE: To use video analysis to provide direct, independent identification of the etiologic and mechanistic variables responsible for TMT dislocations in professional football players. STUDY DESIGN: Case series; Level of evidence, 4. METHODS: Sixteen professional National Football League players who sustained publicly reported TMT dislocations were identified. Publicly broadcast game footage of the plays in which injury occurred was reviewed by a panel of 5 biomechanists. Consensus was reached regarding the details surrounding injury, and a weighting was assigned to each detail based on the panel's confidence. RESULTS: Roughly 90% of injuries occurred while the injured player was engaged with or by another player, a detail that has heretofore been undocumented. Few injuries resulted from direct loading of either the foot or the ipsilateral limb; however, the injured foot was frequently subjected to axial loading from ground engagement with the foot in plantar flexion and the toes dorsiflexed. Injurious loading was often due to external rotation of the midfoot (86%). Fifteen of 16 injuries were season ending. CONCLUSION: TMT dislocations are frequently associated with engagement by or with a second player but infrequently caused by a direct blow to the foot. Axial loading of the foot, external rotation, and pronation/supination are the most common conditions during injurious loading.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".