Weight management and physical activity throughout the cancer care continuum
Bibliographic record
Abstract
Mounting evidence suggests that weight management and physical activity (PA) improve overall health and well being, and reduce the risk of morbidity and mortality among cancer survivors. Although many opportunities exist to include weight management and PA in routine cancer care, several barriers remain. This review summarizes key topics addressed in a recent National Academies of Science, Engineering, and Medicine workshop entitled, "Incorporating Weight Management and Physical Activity Throughout the Cancer Care Continuum." Discussions related to body weight and PA among cancer survivors included: 1) current knowledge and gaps related to health outcomes; 2) effective intervention approaches; 3) addressing the needs of diverse populations of cancer survivors; 4) opportunities and challenges of workforce, care coordination, and technologies for program implementation; 5) models of care; and 6) program coverage. While more discoveries are still needed for the provision of optimal weight-management and PA programs for cancer survivors, obesity and inactivity currently jeopardize their overall health and quality of life. Actionable future directions are presented for research; practice and policy changes required to assure the availability of effective, affordable, and feasible weight management; and PA services for all cancer survivors as a part of their routine cancer care. CA Cancer J Clin 2018;68:64-89. © 2017 American Cancer Society.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".