Outcome measures that matter: exploring the edges of sport and exercise medicine
Bibliographic record
Abstract
As sport and exercise medicine continues to establish itself as a fertile discipline for clinically relevant research, we are making discoveries across a broad spectrum of content—from the harmful effects of sitting to the best injury prevention protocols for elite athletes, and everything in between. There is one area that our community should pay more attention to: implementation. As more laboratory data roll in and theoretical frameworks roll out, we should ask ourselves the fundamental question, ‘Yes, but does it matter?’ Are we moving the needle on physical inactivity? Are we pushing the edges of sport, including who can and should participate? This issue has been curated by the Canadian Academy of Sport and Exercise Medicine (CASEM) and we explore the answers to some of those questions. Wearables are becoming increasingly common, mining ever more detailed data for the user… but does it matter? O’Driscoll et al ( See page 332 ) of Leeds University explore how activity monitors stack up on estimating energy expenditure; among other findings, without heart rate sensors, large …
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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 | 0.251 | 0.423 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".