Arthroscopic labral repair and shoulder stabilization in National Hockey League players are associated with decreased performance in the first year of return to play with return to baseline in the second year
Bibliographic record
Abstract
Background Few studies have assessed performance in National Hockey League players following shoulder labral repair and stabilization using advanced statistics. Our objective was to assess National Hockey League player performance following shoulder labral repair and stabilization Methods National Hockey League players who underwent surgical procedures for labral repair and stabilization between 2008 and 2022 were identified using a publicly available injury database. We obtained demographic and outcome data for one-year preinjury and two years postinjury. Our primary outcome was wins above replacement per 60 minutes played (WAR/60). A matched cohort based on position, draft year, and index season performance was established. Outcomes were compared between cases and controls with a paired t -test. Results We identified 94 eligible patients who underwent shoulder labral repair or stabilization. Preinjury, postinjury year one, and postinjury year two WAR/60 were 0.03, 0.02, and 0.03 compared to 0.03, 0.06, and 0.05 in controls ( P = .33, .00, .07, respectively). Offensive performance was lower both one and two years postinjury when compared to controls ( P = .00, P = .01, respectively). Conclusions Shoulder labral tears and glenohumeral instability requiring surgical management are associated with decreased overall performance one year postsurgery with return to baseline by postinjury year two. Offensive performance remained decreased at the second postinjury year.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.003 | 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".