Bibliographic record
Abstract
Age-Specific Polyp Detection Rates and Characteristics in Ontario's Colorectal Cancer Screening Program Background: While organized colorectal cancer screening programs effectively reduce cancer incidence and mortality in adults aged 50-74, the optimal approach for screening adults aged 75 and older remains unclear. This knowledge gap is particularly relevant in Ontario, where the population aged 65 and older is projected to increase from 2.9 to 4.7 million by 2051. Methods: This retrospective cohort study will analyze Ontario health administrative data from January 2020 to December 2024, comparing polyp detection rates and characteristics between individuals aged 75+ undergoing colonoscopy and historical FIT-positive patients aged 50-74. The study will examine polyp detection rates, characteristics (size, morphology, histology), advanced adenoma prevalence, comorbidity impact, post-colonoscopy complications, and socioeconomic influences. Statistical analyses will include multivariable logistic regression and multinomial regression models. Results: Expected findings will characterize age-specific patterns in polyp detection rates and advanced neoplasia prevalence, providing crucial data for risk stratification. Results will inform evidence-based modifications to current screening protocols and guide resource allocation within Ontario's healthcare system. Conclusions: This study addresses critical knowledge gaps in age-specific colorectal cancer screening outcomes within Ontario's healthcare context. Findings will contribute to resolving guideline inconsistencies and support evidence-based decision-making for screening older adults, ultimately improving colorectal cancer prevention strategies in Ontario's aging population. Keywords: colorectal cancer screening, elderly, polyp detection, colonoscopy outcomes, health administrative data
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".