Association of self-directed walking with toxicity moderation during chemotherapy for the treatment of early breast cancer
Bibliographic record
Abstract
Abstract Background This study investigates associations of activity tracker steps with patient-reported toxicities during chemotherapy. Methods Women with early breast cancer reported their symptom severity every 2–3 weeks throughout chemotherapy treatment and daily steps were documented through a Fitbit activity tracker. Relative risks (RR) and 95% confidence intervals (CI) were calculated using Poisson regression models with robust variance. For outcomes significant in unadjusted models, adjusted RRs were calculated controlling for race (dichotomized White and Non-White), age (10-year increments), and education level. Tracker step cut point (high step, low step) was determined by the mean. Cumulative incidence functions of moderate, severe and very severe (MSVS) symptoms were estimated using the Kaplan-Meier method and compared using a Cox proportional hazard model. Results In a sample of 283 women, mean age was 56 and 76% were White. Mean tracker-documented steps/week were 29,625 (only 20% achieved the goal of 44,000 steps/week), with 55% walking below the mean (low step) and 45% above (high step). In multivariable analysis adjusted for age, race and education, high step patients had lower risk for fatigue [RR 0.83 (0.70,0.99)] (p = .04), anxiety [RR 0.59 (0.42,0.84)] (p = .003), nausea [RR 0.66 (0.46,0.96)] (p = .03), depression [RR 0.59 (0.37,0.03)] (p = .02), and ≥ 6 MSVS symptoms [RR 0.73 (0.54,1.00)] (p = .05). High step walkers also had 36% lower relative risk for dose reductions [RR 0.64, 95% CI 0.43,0.97)] (p = .03). Conclusion Self-directed walking at a rate of at least 30,000 steps/week may moderate the severity of treatment side effects during chemotherapy for early breast cancer.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".