What have we learnt from quantitative case reports of acute lateral ankle sprains injuries and episodes of ‘giving-way’ of the ankle joint, and what shall we further investigate?
Bibliographic record
Abstract
Lateral ankle sprains are a commonly incurred injury in sports. They have a high recurrence rate and can lead to the development of persistent injury associated symptoms. We performed a quantitative synthesis of published case reports documenting the kinematics of acute lateral ankle sprains and episodes of ‘giving-way’ of the ankle joint to provide a comprehensive description of the mechanisms. A systematic literature search was conducted to screen records within MEDLINE® and EMBASE®. Additional strategies included manual search of specific journals, as well as contacting researchers in relevant communities to retrieve unpublished data. Twenty-four cases were included in the quantitative synthesis, 11 from individual case reports and 13 from four separate case series. Two authors independently reviewed all the articles and extracted ankle joint kinematic data. Excessive ankle inversion was the most pronounced kinematic pattern observed across all included cases, with a mean peak inversion angle of 67.5° (range 2.0 to 142) and a mean peak inversion velocity of 974°/s (range 468 to 1752). This was followed by internal rotation and plantar flexion, respectively. A homogeneous linear function revealed a mean inversion velocity across all cases of 337°/s (range 117 to 1400; R2 = 0.78; p < 0.0001).
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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 | 0.112 | 0.464 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.038 | 0.022 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".