Meniscal lesion or patellar tendinopathy? A case report of an adolescent soccer player with knee pain.
Bibliographic record
Abstract
Background: Injuries to the meniscus are particularly prevalent in soccer players, with an incidence of 0.448 injuries per 1000 hours of playing. However, in the adolescent soccer player population, it has been reported that up to 63% of asymptomatic knees may demonstrate horizontal or oblique tears on MRI. These results may negatively influence clinical decision-making and plan of management for adolescent soccer players with knee problems. Case presentation: A case of a 15-year-old soccer player is presented after having been diagnosed by his family physician with a left lateral meniscus tear as per MRI, following a 10-week period of anterior knee pain. He presented to a chiropractor for a second opinion before consulting with the orthopedic surgeon. Management and outcome: Recommendations for progressive rehabilitation owing to the lack of clinical evidence for meniscal abnormality were made. A primary diagnosis of left patellar tendinopathy was determined and after a 6-week comprehensive rehabilitation program, the patient made a complete recovery. Summary: A thorough history, physical examination, and understanding of the patient's injury mechanism are suggested before confirming/refuting suspicions of meniscal abnormalities via MRI. This will help to inform better clinical decision-making as well as decrease the occurrence of unnecessary imaging.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".