A history of smoking does not reduce long-term benefits of cardiac rehabilitation on cardiorespiratory fitness in men and women with cardiovascular disease
Bibliographic record
Abstract
Smoking is an important risk factor for cardiovascular disease and all-cause mortality. Cardiac rehabilitation (CR) is effective for reducing the risk of recurrent cardiac events through improving cardiorespiratory fitness (CRF). Little is known about the influence of smoking on CRF throughout long-term CR. The purpose of this analysis was to compare CRF trajectories among individuals with positive and negative smoking history enrolled in long-term CR. Participants had a positive smoking history if they currently or formerly smoked (Smoke+, n = 55, mean age = 64.9 ± 9.0 years) and had a negative history if they never smoked (Smoke–, n = 34, mean age = 61.4 ± 9.0 years). CRF (peak oxygen uptake) was measured at baseline and annually thereafter for 6 years. The Smoke+ group had lower CRF compared with the Smoke– group over enrollment (β = −3.29 (SE = 1.40), 95% confidence interval (CI) −6.04 to −0.54, p = 0.02), but there was no interaction of smoking history and enrollment (β = 0.35 (SE = 0.21), 95% CI: −0.06 to 0.77, p = 0.10). Moreover, trajectories were not influenced by pack-years (β = 0.01 (SE = 0.01), 95% CI: −0.01 to 0.04, p = 0.23) or time smoke-free (β = −0.002 (SE = 0.01), 95% CI: −0.02 to 0.02, p = 0.80). Although the trajectories of CRF do not appear to be affected by smoking behaviour, individuals without a history of smoking maintained higher CRF throughout enrollment. Novelty: The benefits of long-term exercise-based cardiac rehabilitation on cardiorespiratory fitness are similar between those who have smoked and those who have never smoked. Neither the number of pack-years nor the length of time spent smoke-free influence cardiorespiratory fitness trajectories following long-term cardiac rehabilitation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".