Deimplementing arthroscopy, improving concussion reporting and celebrating research quality
Bibliographic record
Abstract
As we say on the British Journal of Sports Medicine (BJSM) podcast—thanks for choosing to engage with BJSM. Your worldwide author team brings you the full spectrum sport and exercise medicine, sports physiotherapy, as well as sports science in this 22nd of our 24 annual issues. Three articles relate to arthroscopy: the Finnish Degenerative Meniscal Lesion Study (FIDELITY) knee surgery randomised controlled trial (RCT) (see page 1332) was a landmark because it: (i) included a sham surgery control group and, (ii) patients had an MRI-proven meniscal tear along with their symptoms and signs. These were classic patients for arthroscopy—classic in the sense that they very typically were slam-dunks for surgery. In the FIDELITY trial, sham surgery produced great results at 1 year (reported in the New England Journal of Medicine 1) and that sham group continues to do well at 5 years as you can read here. They are doing just as well as the patients who underwent arthroscopic partial meniscectomy. Read two leading orthopaedic surgeons’ take home messages—thank you Drs Lars Engebretsen (Norway) and …
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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 | 0.175 | 0.511 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.028 | 0.025 |
| Insufficient payload (model declined to judge) | 0.032 | 0.020 |
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".