Development of an evidence-informed recommendation guide to facilitate physical activity counseling between oncology care providers and patients in Canada
Bibliographic record
Abstract
Decision support aids help reduce decision conflict and are reported as acceptable by patients. Currently, an aid from the American College of Sports Medicine exists to help oncology care providers advise, assess, and refer patients to physical activity (PA). However, some limitations include the lack of specific resources and programs for referral, detailed PA, and physical function assessments and not being designed following an international gold standard (Appraisal of Guidelines for Research and Evaluation [AGREE] II). This study aimed to develop a recommendation guide to facilitate PA counseling by assessing the risk for PA-related adverse events and offering a referral to an appropriate recommendation. Recommendation guide development followed AGREE II, and an AGREE methodologist was consulted. Specifically, a stakeholder group of oncology care providers and cancer survivors were engaged to develop the assessment criteria for comorbidities, PA levels, and physical function. Assessment criteria were developed from published PA interventions, consultations with content experts, and targeted web-based searches for cancer-specific PA programs. Feedback on the recommendation guide was solicited from stakeholders and external reviewers with relevant knowledge and clinical experience. Independent AGREE methodologists appraised the development process. The recommendation guide is a five-page document, including a preamble, assessment criteria for absolute contraindications to PA, comorbidities, and PA/functional capacity with a list of appropriate resources. Independent AGREE methodologists rated the development process as strong and recommended the guide for use. The recommendation guide has the potential to facilitate PA counseling between oncology care providers and cancer survivors, thus, potentially impacting PA behavior.
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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 | 0.069 | 0.288 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".