Influence of Chronic Disease and Comorbidity on Colorectal Cancer Screening and Diagnostic Testing
Bibliographic record
Abstract
Colorectal cancer (CRC) is a leading cause of morbidity and mortality. Population-based CRC screening has been recommended by in Canada since 2001 and organized screening programs, which involve coordinated activities from screening through diagnosis, have been implemented in most Canadian jurisdictions. Those who have chronic comorbidities may be less likely to participate in all steps of the cancer preventive pathway due to the competing demands of chronic disease management. As some major chronic conditions are also associated with increased cancer morbidity and mortality, understanding the use of cancer preventive services in these populations is necessary to improve the effectiveness of screening programs.This dissertation aimed to determine whether major chronic medical and mental health comorbidities are associated with lower use CRC screening and follow-up diagnostic testing, focusing on the province of Ontario, Canada. The first study was a systematic review and meta-analysis examining the effect of one “model” chronic condition – diabetes – on screening for CRC and other common cancers for which universal screening has been recommended (breast and cervical cancer). The second and third studies were longitudinal population-based cohorts using Ontario health administrative data, with the second study investigating the influence of diabetes, heart disease, renal failure, chronic obstructive pulmonary disease, and mental health conditions on periodic CRC screening test uptake, and the third study considering the effects of these conditions on follow-up diagnostic testing receipt. The first study found that diabetes was associated with lower likelihood of breast and cervical cancer screening, while the results for CRC screening were mixed. There were significant methodological limitations in the evidence base, including the focus on one-time screening in opportunistic settings and the use of cross-sectional designs and self-report data. The second and third studies found that major chronic conditions were associated with lower rates of periodically becoming up-to-date with CRC screening and with receiving follow-up testing. Having multiple medical conditions or comorbid medical and mental health conditions exacerbated these relationships. The findings of this thesis highlight that organized screening programs may need to consider additional strategies to reduce breakdowns in the cancer preventive pathway and improve appropriateness of screening for people with comorbidities.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".