Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Evidence supports a systematic screening programme before participation Which screening strategy should be used to identify young athletes at risk for sudden cardiac death is a highly controversial matter. For many years the medical community has disputed the cost effectiveness, feasibility, and accuracy of including 12 lead electrocardiography in the cardiovascular screening of athletes. Discordant recommendations from the American Heart Association and the European Society of Cardiology have fuelled a global debate about the usefulness of such screening in athletes.1 2 In the linked study, Sofi and colleagues analyse data from 30 065 Italian athletes who underwent a complete pre-participation cardiovascular evaluation including resting and exercise electrocardiography.3 Sudden cardiac death in young athletes (<35 years) is caused by a diverse set of structural diseases of the heart (such as cardiomyopathies) and electrical defects (such as ion channelopathies). In the United States alone, one young competitive athlete dies every three days from an unrecognised cardiovascular disorder.4 American and European authorities have recommended a comprehensive pre-participation evaluation, which includes a detailed patient and family history and a physical examination, in all athletes of 12 years or more.1 2 Warning symptoms of underlying …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it