Identification and prediction of physical activity trajectories in women treated for breast cancer
Bibliographic record
Abstract
PURPOSE: In this study, we aimed to identify trajectories of physical activity in a cohort of women over a 1-year period after treatment for breast cancer. We also examined factors that could predict trajectory group membership. METHODS: We collected data from 199 women using questionnaires at baseline (mean = 3.46 months after treatment), and 3, 6, 9, and 12 months thereafter. RESULTS: Based on semiparametric group-based modeling, there were five trajectories: consistently inactive, decreasing levels, inactive with increasing levels, somewhat active, and consistently sufficiently active. Based on logistic regression analysis, women who reported higher levels of depressive symptoms and fatigue were less likely to remain consistently sufficiently active, and women who reported higher levels of cancer worry were more likely to remain consistently sufficiently active. Age, stage of cancer, time since treatment, number of treatment types received, and number of physical symptoms did not predict trajectory group membership. CONCLUSIONS: Women do not have uniform physical activity trajectories after treatment for breast cancer. Identification subgroups of women who do not remain consistently sufficiently active, and factors that predict these trajectories, can aid in the development of targeted behavior change interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".