Exploring Under and Overscreening to Address the Public Health Burden of Colorectal Cancer
Bibliographic record
Abstract
Background: Cancer screening is only effective if eligible individuals participate in screening and do so at the recommended intervals. The objective of this dissertation was to explore two challenges in cancer screening: 1) strategies to mitigate non-participation (Studies 1 & 2); and 2) risk reduction after a complete colonoscopy (Study 3) to maximize patient outcomes and minimize risks for colorectal cancer (CRC). Methods: Study 1 included a systematic review and meta-analysis of randomized controlled trials or quasi-experimental studies evaluating the effectiveness of social media and mobile health (mHealth) interventions. In Study 2, we conducted a qualitative descriptive study with Facebook users of screen-eligible age to develop social media messages promoting CRC screening. Finally, we conducted a population-based retrospective cohort study to explore the association of complete colonoscopy with CRC incidence and mortality (Study 3) and the duration of risk reduction. A time to event analysis using a Cox-proportional hazards regression model with time-varying covariates was used to generate adjusted estimates. Results: In Study 1, we identified a total of 39 studies and the overall pooled odds ratio for screening participation was 1.49 (95% CI: 1.31–1.70) with effect sizes similar across all cancer types. In Study 2, we developed recommendations for 7 messages; 1 was classified as strongly consider, 4 as consider using this message and 2 as proceed with caution. Participants preferred social media messages that were believed to be credible, educational, and with a positive or reassuring tone. Finally, exposure to a complete negative colonoscopy was significantly associated with a lower risk of disease for more than 15 years (HR 0.710; 95% CI: 0.581-0.867 for females and HR 0.541; 95% CI: 0.436-0.672 for males) in comparison to those without a complete colonoscopy. A similar trend was observed for CRC-related mortality. Conclusions: Screening programs should consider incorporating mHealth and social media into their efforts to increase uptake. We have a provided a strong foundation of theory-informed social media messages that may be preferred by the target population. Finally, our data also suggest that more prolonged intervals than recommended by some guidelines could be considered after a complete negative colonoscopy.
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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 | 0.025 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".