Effects of cigarette smoke exposure on pulmonary physiology, muscle strength and exercise capacity in a retrospective cohort with 30,000 subjects
Bibliographic record
Abstract
BACKGROUND: The effects of long-term cigarette smoke exposure on pulmonary physiology and how those effects lead to reduced exercise capacity are not well established. METHODS: We retrospectively analyzed the spirometry, single-breath gas transfer (DLCO), peripheral muscle strength, and maximum exercise capacity data in patients referred to McMaster University Medical Centre for cardiopulmonary exercise testing between 2000 and 2012. RESULTS: 29,441 subjects underwent CPET and had a recorded smoking history [58% male, mean age 51.1 years (S.D.±19.6), BMI 27.4 kg/m2(±5.8)]. 7081 (24%) were current or former smokers and were divided into 4 categories by packs years (mean ±S.D.): <10 (5.8±3.3), 10-20 (17.1±2.9), 20-30 (27.1±2.8), 30-40 (37.3±2.8), and >40 (53.9±12.8). Patients with greater cigarette smoke exposure had lower expiratory flow rates (FEV1, FEF50, FEF75, PEFR), DLCO, and maximum power output (MPO) during exercise. There was no association between smoke exposure and muscle strength. Modeling MPO (kpm/min) output as a function of demographic and physiologic variables showed that the data are well explained by muscle strength (kg), FEV1 (L), and DLCO (mmHg/min/mL) in similar magnitude (MPO = 42.7*Quads0.34*FEV10.34 * DLCO0.43; r = 0.84). CONCLUSIONS: Long-term cigarette smoke exposure is associated with small airway narrowing and impaired diffusion capacity but not with peripheral muscle weakness. The effects of smoking, age, and gender on maximum power output are mediated by reductions in FEV1, muscle strength and DLCO. Exercise capacity in smokers may benefit from therapies targeting all 3 variables.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".