Performance in National Hockey League Players Decreases 1 Year After Undergoing Hip Arthroscopy With Return to Baseline by the Second Year
Bibliographic record
Abstract
Purpose To evaluate performance following hip arthroscopy in National Hockey League (NHL) players. Methods NHL players who underwent hip arthroscopy between 2008 and 2022 were identified through 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 measure was wins above replacement per 60 minutes played (WAR/60), with secondary outcomes, including offensive and defensive goals, above replacement per 60 minutes played. A matched cohort was created on the basis of position, draft year, and index season performance. Preinjury and postinjury outcomes were compared using a paired t test. Results Twenty‐eight players underwent 29 eligible procedures. Preinjury, postinjury year 1, and postinjury year 2 WAR/60 were 0.06, 0.01, 0.04, respectively, in cases ( P = .01, P = .12 compared to preinjury) and 0.07, 0.06, and 0.04, respectively, in controls ( P = .35, P = .01, P = 0.82) compared to cases. Offensive goals above replacement per 60 minutes played were decreased compared to presurgery 1 and 2 years postsurgery but only performance 2 years postsurgery met statistical significance ( P = .55, P = .03). Defensive goals above replacement per 60 minutes played were decreased compared to presurgery at postsurgery year 1 and 2, but only performance in year 1 was statistically significant ( P = 0.01, P = .54). Postinjury defensive goals above replacement per 60 minutes played were significantly decreased compared to controls at postinjury year one ( P = .03). Conclusions NHL players who have undergone hip arthroscopy are associated with significantly decreased performance 1 year following surgery when compared with controls, driven primarily by decreased defensive performance. However, performance of major analytic metrics recovers by postinjury year 2. Level of Evidence Level III, retrospective 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".