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Record W4404858307 · doi:10.1177/24730114241300153

Achilles Tendon Ruptures in National Hockey League Players: Return to Sport and Performance Impact

2024· article· en· W4404858307 on OpenAlexaff
Emmitt Hayes, Bradley Meulenkamp, Bogdan A. Matache, Michael Pickell

Bibliographic record

VenueFoot & Ankle Orthopaedics · 2024
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineAchilles tendonTearsLeaguePhysical therapyIce hockeyAthletesSurgeryTendonPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.299
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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