Bibliographic record
Abstract
There is a saying that “what goes around comes around”, and exercise training as a treatment for patients suffering from coronary heart disease is no exception to the rule. The eighteenth century English physician, William Heberden, recorded the case of a patient suffering from angina “who set himself the task of sawing wood every day and was nearly cured”.1 Almost a century later in 1854, the Irish doctor, William Stokes wrote “the symptoms of debility of the heart are often removable by a regulated course of gymnastics, or by pedestrian exercise”.2 His “pedestrian cure” consisted of comfortable walking initially on level ground, the distance and gradient being increased as tolerance improved—always, however, cautioning against excessive fatigue, breathlessness, or chest pain. Have we progressed that far since then? Over the ensuing years, Stokes' exercise training regime was largely forgotten, obscured by the teaching of the London surgeon John Hilton, who stressed the value of strict bed rest.3 Unfortunately Hilton's precept was carried to extremes. Prolonged immobilisation in bed became the cornerstone …
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".