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Record W4413982990 · doi:10.18176/archmeddeporte.00200

Return to play after tearing an anterior cruciate ligament in high-contact sports

2025· article· en· W4413982990 on OpenAlexaboutno aff
Íñigo Úbeda-Pérez de Heredia, Julio Barrera-Torres

Bibliographic record

VenueArchivos de Medicina del Deporte · 2025
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsnot available
Fundersnot available
KeywordsTearingAnterior cruciate ligamentReturn to sportOrthodonticsPhysical medicine and rehabilitationMedicineAnatomyPhysical therapyEngineeringAthletesMechanical engineering

Abstract

fetched live from OpenAlex

Introduction: Know the return to play of Rugby, American football, Australian football and Canadian football players who underwent plastia of the anterior cruciate ligament. Material and method: A systematic review of the publications accessible in scientific databases was carried out between 2013 and 2023. After establishing inclusion and exclusion criteria, two blocks were designed based on the sports modality: Group 1: American and Canadian football players. Group 2: rugby and Australian rules football players. A sample of 3,395 athletes was obtained, of which 2,337 belonged to the first group and 1,058 to the second. The results were evaluated based on age, sex, sport type and time of return to competition. Results: Group 1 is made up of 2,337 players, of which 1,541 (65.94%) returned to the competition, with an average time of 11.37 months. In group 2, 1,058 players were included, of which 855 (80.81%) joined sports activity prior to the injury with an average of 9.21 months. Conclusions: Professional American and Canadian football players have a higher rate of abandonment of their sporting activity and, in cases where they return to competition, the time is higher than that of professional rugby and Australian football players.

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)0.001
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 score0.280
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.005
GPT teacher head0.275
Teacher spread0.270 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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