What have overprescription of NSAIDs, overtraining and modelling physical activity for kids got to do with Barack Obama in Berlin?
Bibliographic record
Abstract
September already! Can you believe it? Are you exhausted from the Olympics? Has it motivated your patients to be active? Interestingly, when Australians were interviewed before and just after the Sydney Games, there was an increase in people thinking about doing physical activity. Unfortunately, a year later physical activity levels had remained unchanged.1 Not to worry, the Olympics contribute to world peace. So we eagerly anticipate more peace during upcoming Olympics in Vancouver (2010), London (2012) and, we hope, Tromso (2018). But on to BJSM business and Barack. In this issue we learn that 10% of FIFA World Cup players took NSAIDs prior to every match ( see p 725 )! Would paracetamol/acetaminophen have been better choices? Or a placebo? As sports clinicians working with the “healthy elite athlete” is this OK? Futhermore, supplement use was rampant—the champion ingester took 7.4 different supplements before every game! Clearly this player couldn’t play in the Football Association for the Visually Impaired (http://www.favi.co.uk) because his stomach rattle would have him confused for the ball. This is a landmark paper not only for athlete health but also for the terrific collaboration among team physicians—Hippocrates would approve. Fortunately the supplement creatine does not produce liver or kidney damage ( see p 731 ). Good thing. But the question remains—does it provide athletes any benefit? Pluim argued that there is no evidence for its use in tennis.2 Another expensive placebo?3 Share your thoughts on the BJSM Blog http://blogs.bmj.com/bjsm/. The second cover page controversy relates to overtraining/overreaching. IOC Medical Commission Chairman, …
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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 | 0.003 | 0.019 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".