Return to play after tearing an anterior cruciate ligament in high-contact sports
Bibliographic record
Abstract
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 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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".