Personal Navigation Increases Colorectal Cancer Screening Uptake
Bibliographic record
Abstract
BACKGROUND: Prior randomized, controlled trials (RCTs) indicate that patient navigation can boost colorectal cancer screening rates in primary care. The sparse literature on pragmatic trials of interventions designed to increase colorectal cancer screening adherence motivated this trial on the impact of a patient navigation intervention that included support for performance of the participants' preferred screening test (colonoscopy or stool blood testing). MATERIALS AND METHODS: Primary care patients (n = 5,240), 50 to 74 years of age, with no prior diagnosis of bowel cancer and no record of a recent colorectal cancer screening test, were identified at the Group Health Centre in northern Ontario. These patients were randomly assigned to an intervention group (n = 2,629) or a usual care control group (n = 2,611). Intervention group participants were contacted by a trained nurse navigator by telephone to discuss colorectal cancer screening. Interested patients met with the navigator, who helped them identify and arrange for performance of the preferred screening test. Control group participants received usual care. Multivariate analyses were conducted using medical records data to assess intervention impact on screening adherence within 12 months after randomization. RESULTS: Mean patient age was 59 years, and 50% of participants were women. Colorectal cancer screening adherence was higher in the intervention group (35%) than in the control group (20%), a difference that was statistically significant (OR, 2.11; confidence interval, 1.87-2.39). CONCLUSION: Preference-based patient navigation increased screening uptake in a pragmatic RCT. IMPACT: Patient navigation increased colorectal cancer screening rates in a pragmatic RCT in proportions similar to those observed in explanatory RCTs.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".