Walking-to-Jogging Transition Criteria after Anterior Cruciate Ligament Reconstruction in Soccer Players: A Narrative Review
Bibliographic record
Abstract
Objective. To identify the criteria for transition from walking to jogging training in soccer players after ACL reconstructive surgery. Methods. We have conducted a literature synthesis process using Arksey & O’Malley’s methodology to answer precise research questions. The questions identified for conducting it were: A) What are the clinical criteria and the timing for prescribing return to running after anterior cruciate ligament reconstruction in soccer players? B) What are the postural/functional criteria for prescribing a return to jogging after ACL reconstruction in soccer players? To answer these questions, we performed a literature search of the last six years (January 2018- February 2024) on Web of Science, PEDro, SPORTDiscus, Google Scholar, and PubMed electronic biomedical database. Results. The return to jogging is not related to the time of surgery but to the achievement of the following goals: a) Complete range of motion (ROM) of the knee; b) No pain; c) No effusion (swelling); d) IKDC >90/100; e) Triple crossover hop test of uninjured side; f) Single leg hop tests >90% of uninjured side; g) Single leg squat test or step up without increase in knee valgus/varus; h) Trendelenburg sign negative; i) Pelvic drop negative; l) physiological ratio Q/H; m) Sufficient quality of movement during foundation technical and athletic skills. The timing for return to jogging is about 12 weeks after reconstructive surgery. Conclusions. Transitioning from walking to jogging training requires the athlete to achieve precise clinical, rehabilitation, and postural/functional objectives.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".