Effect of supervised exercise training on objectively measured physical activity in patients during anthracycline therapy
Bibliographic record
Abstract
It is unknown what the benefits of cardio-oncologic rehabilitation programmes on cardiorespiratory fitness and cancer fatigue during anthracycline-based chemotherapies (AC) are with supervised exercise training (ET), compared to PA advice and tracking only. Patients with breast cancer or lymphoma were recruited from four cancer centres and randomly assigned to three months supervised ET during (EXduringAC) or after (EXpostAC) AC. All patients were counselled on physical activity (PA) and PA was objectively measured with an activity tracker. Primary endpoints were peak VO 2 , fatigue and quality of life (QoL) after AC (AC-end) and at follow-up (3 months after completion of AC). Secondary endpoints were daily PA and daily steps during AC and follow-up phase, which were compared between days with and without centre-based training sessions. All analyses were performed by linear mixed models. Fifty-seven patients (median [1st and 3rd quartiles] age 47 years [38, 57 years]; 95 % women) consented to participate, of whom data from 51 patients were available. Despite the fact that PA on days with centre-based training sessions was 28 (95 % confidence interval 24–32) min higher with 4382 (3995–4768) more steps, neither PA nor steps differed between groups in neither AC nor follow-up phase, nor were there between group differences in peak VO 2, QoL or fatigue at any time point. In physically active patients with cancer, PA advice and using an activity tracker was equally effective on changes in peak VO 2 , fatigue, or QoL as enrolling in centre-based ET performed during or after AC. NCT03850171, February 21, 2019.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".