Heart Disease, Physical Activity Trajectories, and Gender
Bibliographic record
Abstract
Objectives: The first purpose was to identify distinct physical activity growth trajectories for light and moderate to vigorous physical activity (PA) after hospitalization for heart disease (i.e., to identify sub-groups of patients whose PA trajectories are similar to each other, but different from patients in other PA trajectory groups). The second purpose was to determine if gender predicted sub-group membership for each PA intensity. Design and Setting: Participants (N=554) completed a questionnaire in hospital and at 2, 6, 12, and 24 months after hospitalization. Results: Latent class growth curve analyses showed two classes of patients emerged for light intensity PA that were labeled Inactive Maintainers-Light (72.2% of the sample) and Low Active Maintainers-Light (27.8%). For moderate to vigorous PA, 87.8% of the sample was labeled Inactive Maintainers (i.e., remained inactive for the entire 2-year period), whereas 12.2% were labeled Active Maintainers (i.e., remained active for the 2-year period). Gender did not predict light intensity PA group membership, however, females were significantly more likely to be in the Inactive Maintainer for moderate to vigorous PA compared to males (odds ratio = 3.47). Conclusion: The association between gender and PA trajectories after hospitalization for heart disease may be intensity dependent.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".