Achilles Tendon Ruptures in National Hockey League Players: Return to Sport and Performance Impact
Bibliographic record
Abstract
Background: Few studies assess rates of return to play and postinjury performance in National Hockey League (NHL) players who sustain Achilles tendon ruptures. Our objective was to determine the rate of return to play and performance impact among NHL players who undergo surgical repair of Achilles tendon tears. Methods: NHL players who sustained an Achilles tendon rupture between 2001 and 2021 were identified using a publicly available injury database. Demographic and outcome data were collected for the 1-year period preceding and the 2-year period following surgery. Our primary outcome was expected wins above replacement per 60 minutes played. A position, draft year, and index season performance matched cohort was created. Pre- and postinjury outcomes were compared between cases and controls with a paired t test. Results: We identified 15 cases (9 forwards, 5 defencemen, 1 goaltender). Fourteen of 15 (93%) players returned to play. Preinjury, postinjury year 1, and postinjury year 2 expected wins above replacement were 0.05, 0.05, 0.05 respectively ( P > .05). There was no significant difference in performance between cases and controls at any time point. Conclusion: Achilles tendon tears are associated with a high rate of return to play in the NHL and are not associated with a significant change in offensive, defensive, or overall performance-based metrics. Level of Evidence: Level III, case-control study.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".