How effective are colorectal cancer screening programs at increasing the rate of screening in asymptomatic average-risk groups in Canada?
Bibliographic record
Abstract
Colorectal Cancer (CRC) is the third most commonly diagnosed cancer and second highest cause of cancer related mortality in Canada. Despite availability of screening services and establishment of guidelines, utilization of screening procedures in Canada has historically been low. ColonCancerCheck is an organized colorectal cancer screening program introduced in Ontario in 2008 which aims to increase screening adherence. The objectives of this study are to estimate the impact of ColonCancerCheck on screening behavior in the overall asymptomatic average risk population, examine demographic predictors of screening, and investigate how these are modified. Additionally, this analysis measures the effectiveness of ColonCancerCheck in how it modifies age-specific screening rates at the recommended initiation age of 50 years. This analysis uses data from five cycles of the Canadian Community Health Survey prior to and following program implementation. Survey cycles were pooled to create one average pseudo-population and bootstrap repeated replication techniques were applied for accurate variance calculation. A difference-in-differences design was used to evaluate the overall impact of ColonCancerCheck in Ontario relative to the rest of Canada and a regression discontinuity design was used to measure changes in screening rates at age 50. Outcomes include self-report of guaiac fecal occult blood test in the previous year or colonoscopy or flexible sigmoidoscopy in the previous year. Results indicate that factors which consistently influence the likelihood of screening include being physically active, having taken a flu shot, being of an older age, and having a regular medical doctor and greater numbers of physician consultations. The difference in differences analysis indicated that ColonCancerCheck has significantly increased screening in the average risk population although there is insufficient evidence to say that it has altered the demographic predictors of screening. Additionally, results from the regression discontinuity design indicate that screening rates at the age threshold of 50 in Ontario following the implementation of ColonCancerCheck increased significantly although further analysis was inconclusive as to the program causing this increase.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".