Swimming in H<sub>2</sub>O: two parts heart + one part obsession
Bibliographic record
Abstract
The ‘H2O’ swimming slogan illustrates the life of the competitive swimmer as one that is driven by devotion and passion. For the aquatic team physician, this slogan begs the questions: What exactly do we know about the adaptations of the swimmer's heart to the years of endurance training? How is the elite swimmer's heart different to population norms? What else should we be doing to preserve the swimmer's health (figure 1)? Figure 1 Competitive swimming. Swimming is rich with legendary stories of successful athletes: the eight Olympic gold medals of Michael Phelps, the renowned feats of Ian the ‘Thorpedo’ and the television success of Tarzan ‘Johnny Weissmuller’, who won both swimming and water polo Olympic medals. But sadly, not all careers have a fairy-tale ending as swimmers’ careers are often ended prematurely by preventable sport-related injury or illness. Although uncommon, elite swimmers also suffer from sudden cardiac death, as evident in the 2012 death of the 100 m breast stroke world record holder, Alexander Oen. The Olympic Charter obliges all International Federations to encourage and support measures to protect the health of athletes. The Olympic Movement Medical Code further expands these health protection mandates, which are also reflected in the Federation …
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".