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Record W3023328247 · doi:10.1177/0363546520918224

Return to Play and Career Length After Anterior Cruciate Ligament Reconstruction Among Canadian Professional Football Players

2020· article· en· W3023328247 on OpenAlexaffabout
Robert Longstaffe, Jeff Leiter, Tanner Gurney-Dunlop, Robert G. McCormack, Peter B. MacDonald

Bibliographic record

VenueThe American Journal of Sports Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsUniversity of British ColumbiaUniversity of SaskatchewanUniversity of ManitobaPan Am Clinic
Fundersnot available
KeywordsMedicineFootballAnterior cruciate ligamentAthletesACL injuryAnterior cruciate ligament reconstructionLeaguePhysical therapyRehabilitationFootball playersHamstringSports medicineSurgery

Abstract

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Background: For many athletes, a tear of the anterior cruciate ligament (ACL) represents a significant injury that requires a prolonged period away from the sport with substantial rehabilitation. Hypothesis: There will be no difference in return to play (RTP) and career length after hamstring tendon (HT) ACL reconstruction in a group of Canadian Football League professional players as compared with what has been already been reported in the literature among professional football players. Study Design: Case-control study; Level of evidence, 3. Methods: Data on athletes who sustained an ACL injury were collected by team physicians and head athletic trainers from 2002 to 2017 from 2 Canadian Football League teams. Patient details included age at the time of injury, initial injury date, position, practice versus game injury, and primary versus rerupture with injury-specific data, such as affected limb, concomitant injuries, graft choice, and procedure performed. RTP rates and career length data were collected through publically available internet sources. Comparisons between the non-RTP and RTP groups were made with independent-sample t tests. Binomial logistic regression was performed to determine variables (ie, games preinjury, graft type, meniscal injury, collateral ligament injury) that contributed to players not being able to RTP. Results: A total of 44 ACL reconstructions were performed over the study period (HT, n = 32 [72.7%]; bone–patellar tendon–bone [BPTB], n = 8 [18.2%]; allograft, n = 4 [9.1%]). Overall, 69.8% (n = 30) were able to RTP in at least 1 game, while 30.2% (n = 13) did not return. Mean time to return was 316.1 days (range, 220-427 days), or 10.4 months. For those players who did RTP, mean career length after ACL reconstruction was 2.8 seasons, or 34.4 games. The majority (56.8%) of injuries occurred early in the season. Breakdown by graft type demonstrated RTP rates among HT, BPTB, and allograft of 64.5% (n = 20), 87.5% (n = 7), and 75% (n = 3), respectively. Career length among HT, BPTB, and allograft was 2.9, 2.4, and 3 seasons. Logistic regression analysis found only concomitant medial collateral ligament (MCL) injuries to be a negative predictor for RTP. Meniscal injuries were associated with a decreased RTP rate and career length, but this was not statistically significant. Conclusion: The RTP rates after ACL reconstruction in this study are similar to those reported in National Football League players. A concomitant injury to the MCL injury was a negative predictor of RTP. Meniscal injuries demonstrated a trend for decreased RTP rate and career length, but this was not a significant predictor. A large portion of injuries occur early in the season, and further study should be done to examine potential preventative strategies to reduce ACL injuries.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.178
Threshold uncertainty score0.359

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.247
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), 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".

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Citations29
Published2020
Admission routes2
Has abstractyes

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