Low-frequency exercise training improves cardiovascular fitness and strength during treatment for breast cancer: a single-arm intervention study
Bibliographic record
Abstract
Abstract Aerobic and resistance exercise during and after cancer treatment are important for health-related outcomes, however treatment-specific barriers may inhibit adherence. We explored the effect of lower-frequency exercise training on fitness, body composition, and metabolic markers (i.e. glucose and lipids) in a group of recently diagnosed breast cancer patients. Fifty-two females ≥ 18 years with stage I–IIIB breast cancer were instructed to attend 2 cardiovascular and strength training sessions/week over 12 weeks, but program length was expanded as needed to accommodate missed sessions. Pre- and post-intervention, we measured: (1) cardiovascular fitness, (2) isometric strength, (3) body composition (dual-energy X-ray absorptiometry), and (4) fasting glucose, insulin, c-peptide, and lipids. Pre-intervention, participants were 53 ± 10 years old (mean ± SD) and overweight (BMI: 27.5 ± 5.4 kg m −2 , 40.1 ± 6.5% body fat). Forty participants completed the program over a median 20 weeks (range: 13–32 weeks, median frequency: 1.2 sessions/week), over which predicted VO 2 peak improved by 7% (2.2[0.1–4.4] mL/kg/min) (delta[95% CI]), and strength increased by 7–9% (right arm: 2.3[0.1–4.5] N m; right leg: 7.9[2.1–13.7] N m; left leg: 7.8[1.9–13.7] N m). Body composition and metabolic markers were unchanged. An exercise frequency of 1.2 sessions/week stimulated significant improvements in fitness, and may represent a practical target for patients during active treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".