The impact of muscle strength on exercise capacity and symptoms
Bibliographic record
Abstract
The capacity to exercise is a major contributor to functional limitation and is accompanied by increased morbidity and mortality. What are the most important physiological contributors to exercise capacity? Cross-sectional data from consecutive patients referred to the McMaster University Medical Centre exercise laboratory for incremental cardiopulmonary exercise testing from 1988 to 2012 were analysed. Exercise capacity was determined by maximal power output (MPO) in kpm·min−1. The contributions of quadriceps strength (maximal peak force in kg using maximal dynamic voluntary contractions against hydraulic resistance), inspiratory muscle strength (determined using maximal inspiratory pressure (MIP)), maximal breathing capacity (MBC) and gas exchange (carbon monoxide transfer coefficient (KCO)) were determined using regression coefficients in a multiple linear regression model. Dyspnoea and leg fatigue were measured using the modified Borg scale. Contributors to dyspnoea and leg fatigue were assessed using nonlinear regression. A total of 36 389 patients were included (60% male, mean±sdage 53±18 years). Mean±sdMPO, quadriceps strength and MIP achieved were 792±333 kpm·min−1, 46±18 kg and 75±31 cmH2O, respectively. MIP and quadriceps strength accounted for over half the variation in MPO (R2=0.57). Quadriceps strength was a stronger predictor of MPO (standardised regression coefficient, β±se0.37±0.005) than MBC (β±se0.16±0.005) andKCO(β±se0.16±0.004), when adjusted for age, sex, height and weight. The effort required to cycle and breathe at any given power intensified systematically as both respiratory and peripheral muscle strength declined. Muscle weakness causes exercise intolerance and should be routinely assessed in patients presenting with fatigue and dyspnoea, and those with functional limitation both in the presence or absence of disease.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".