DOI 10.1007/s00421-006-0387-2 LETTER TO THE EDITORS
Bibliographic record
Abstract
Experts of controlled clinical trials argue that conclusions about the eVects of medical interventions should be based on clinically relevant outcomes and not on the surrogates such as laboratory measurements. There are several examples in which the eVect on a clinically relevant outcome considerably diverged from the eVect on a surrogate end point (Fleming and DeMets 1996; Rothwell 2005). In this respect, the recent paper by Davison and Gleeson (2006) is somewhat problematic. Davison and Gleeson motivated their study by noting that heavy exertion or long-duration exercise may increase the incidence of upper respiratory tract infection (URI) and by citing two trials in which vitamin C reduced the risk of URI associated with marathon runs. Evidence that vitamin C supplementation may be beneWcial for people who are under heavy physical stress is, however, substantially stronger than Davison and Gleeson present. A recent Cochrane meta-analysis focusing on vitamin C and the common cold found six placebo-controlled trials with participants under heavy acute physical stress (combined N = 642). In this group of trials, vitamin C reduced common cold risk by 50 % (95% CI: ¡34 to ¡62%) (Douglas and Hemilä 2005; Hemilä 2006). Four of the trials, including those cited by Davison and Gleeson, were carried out with marathon runners, the Wfth with Canadian soldiers in a winter exercise (Sabiston and Radomski 1974), and the sixth with schoolchildren in a skiing camp in the Swiss Alps
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.017 | 0.014 |
| Insufficient payload (model declined to judge) | 0.196 | 0.158 |
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".