Bibliographic record
Abstract
Abstract A good deal of controversy has occurred over the safety of exercise in various population groups. Because Americans have gone on a health binge, with millions jogging and entering in organized runs, some understanding of the risks involved warrant discussion. Some of these same issues pertain to the prescribing of exercise and exercise testing. Shephard, of the University of Toronto, has taken the position that a certain level of risk is involved in initiating an exercise program for a sedentary asymptomatic middle-aged man. He advises against an exercise test because of the evidence that a high percentage of abnormal electrocardiographic (ECG) stress tests are falsepositives in this population, and he believes the information may lead to other unnecessary tests, such as angiography. The American Heart Committee on Exercise, however, recommends stress testing prior to the initiation of exercise programs in normals older than 40 years of age or in others with risk factors for coronary artery disease. Fletcher and colleagues and others concur in this decision. Data from the Seattle Heart Watch study clearly indicate that in asymptomatic persons with two or more risk factors, stress testing can identify a cohort with a risk of a coronary event at least 15 times greater than the negative responders.
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.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.016 |
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".