Twenty year follow-up of ACL reconstruction (<i>AJSM</i>)—the evidence of experience
Bibliographic record
Abstract
As clinicians and academics, we are challenged to sift through a myriad of publications with the key goal and purpose of assessing whether the quality of the data and research is good enough to have an impact on the way we practice and on our responsibility to optimise the care of our patients.1–6 Various categorisations of the quality of evidence are available with one of the classics being level of evidence 1 used for high-quality randomised control trials, level of evidence 2 for prospective cohort studies, level of evidence 3 for cohort studies, level of evidence 4 for descriptive case series, and level of evidence 5 for ‘expert’ opinion. For some, systematic reviews and meta-analyses have taken a pre-eminent place in the hierarchy of evidence superseding even a high-quality, well-targeted, randomised control trial. However, one must bear in mind that the weak link of any systematic review/meta-analysis is the fact that the authors may not have performed …
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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 | 0.008 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 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".