Misunderstanding the Female Athlete Triad: Refuting the IOC Consensus Statement on Relative Energy Deficiency in Sport (RED-S)
Bibliographic record
Abstract
Background Smoking contributes to socioeconomic inequalities in mortality, but the extent to which this contribution has changed over time and driven widening or narrowing inequalities in total mortality remains unknown. We studied socioeconomic inequalities in smoking-attributable mortality and their contribution to inequalities in total mortality in 1990–1994 and 2000–2004 in 14 European countries. Methods We collected, harmonised and standardised population-wide data on all-cause and lung-cancer mortality by age, gender, educational and occupational level in 14 European populations in 1990–1994 and 2000–2004. Smoking-attributable mortality was indirectly estimated using the Preston-Glei-Wilmoth method. Results In 2000–2004, smoking-attributable mortality was higher in lower socioeconomic groups in all countries among men, and in all countries except Spain, Italy and Slovenia, among women, and the contribution of smoking to socioeconomic inequalities in mortality varied between 19% and 55% among men, and between −1% and 56% among women. Since 1990–1994, absolute inequalities in smoking-attributable mortality and the contribution of smoking to inequalities in total mortality have decreased in most countries among men, but increased among women. Conclusions In many European countries, smoking has become less important as a determinant of socioeconomic inequalities in mortality among men, but not among women. Inequalities in smoking remain one of the most important entry points for reducing inequalities in mortality.
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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 | 0.023 | 0.097 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.049 | 0.048 |
| Insufficient payload (model declined to judge) | 0.008 | 0.010 |
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".