Video Analysis of Acute Hamstring Injury Mechanisms During Deadlifts
Bibliographic record
Abstract
Background.Existing studies on the mechanisms leading to acute hamstring injury are limited by reliance on author extrapolation and patient recall for injury details.Objectives.This study aims to determine potential mechanisms for acute hamstring strain injuries during deadlifts via videographic observations in vivo.Methods.Videos were searched on the website "YouTube.com"using each of the phrases "hamstring rupture", "hamstring tear", "hamstring injury", "hamstring strain", and "hamstring pull", combined individually with the terms "deadlift", "powerlifting", and "competition".An orthopaedic surgeon validated 16 video clips based on pre-set criteria.Results.16 injury events were analyzed.Hip flexion (n=11) and knee semi-flexion (n=16) were the most common positions leading to injury.The most common injury pattern was a combination of hip flexion with knee semi-flexion and eccentric hamstring loading (n=11).Concentric hamstring loading was observed leading to injury in 3 cases.Conclusion.Acute hamstring injuries during deadlifts occurred by eccentric hamstring loading with a semi-flexed knee and a flexed or semi-flexed hip, or by concentric hamstring loading with a semi-flexed knee and semi-flexed hip.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".