Opportunities to improve quality of care for cancer survivors in primary care: findings from the BETTER WISE study
Bibliographic record
Abstract
PURPOSE: The BETTER WISE (Building on Existing Tools to Improve Chronic Disease Prevention and Screening in Primary Care for Wellness of Cancer Survivors and Patients) intervention is an evidence-based approach to prevention and screening for cancers and chronic diseases in primary care that also includes comprehensive follow-up for breast, prostate and colorectal cancer survivors. We describe the process of harmonizing cancer survivorship guidelines to create a BETTER WISE cancer surveillance algorithm and describe both the quantitative and qualitative findings for BETTER WISE participants who were breast, prostate or colorectal cancer survivors. We describe the results in the context of the COVID-19 pandemic. METHODS: We reviewed high-quality survivorship guidelines to create a cancer surveillance algorithm. We conducted a cluster randomized trial in three Canadian provinces with two composite index outcome measured 12 months after baseline, and also collected qualitative feedback on the intervention. RESULTS: There were 80 cancer survivors for whom we had baseline and follow-up data. Differences between the composite indices in the two study arms were not statistically significant, although a post hoc analysis suggested the COVID-19 pandemic was a key factor in these results. Qualitative finding suggested that participants and stakeholders generally viewed BETTER WISE positively and emphasized the effects of the pandemic. CONCLUSIONS AND IMPLICATIONS FOR CANCER SURVIVORS: BETTER WISE shows promise for providing an evidence-based, patient-centred, comprehensive approach to prevention, screening and cancer surveillance for cancer survivors in the primary care setting. TRIAL REGISTRATION: ISRCTN21333761. Registered on December 19, 2016, http://www.isrctn.com/ISRCTN21333761 .
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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 | 0.014 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".