Participation and Ease of Use in Colorectal Cancer Screening: A Comparison of 2 Fecal Immunochemical Tests
Bibliographic record
Abstract
INTRODUCTION: The impact of fecal immunochemical test (FIT)-based colorectal cancer (CRC) screening on disease incidence and mortality is affected by participation, which might be influenced by ease of use of the FIT. We compared the participation rates and ease of use of 2 different FITs in a CRC screening program. METHODS: There were two study designs within the Dutch CRC screening program. In a paired cohort study, all invitees received 2 FITs (OC-Sensor, Eiken, Japan, and FOB-Gold, Sentinel, Italy) and were asked to sample both from the same stool. Ease of use of both FITs was evaluated by a questionnaire. In a randomized controlled trial, invitees were randomly allocated to receive one of the 2 FITs to compare participation and analyzability. RESULTS: Of 42,179 invitees in the paired cohort study, 21,078 (50%) completed 2 tests and 20,727 (98%) returned the questionnaire. FOB-Gold was reported significantly easier to use. More participants preferred FOB-Gold (36%) than OC-Sensor (5%), yet most had no preference (59%; P < 0.001). In the randomized trial, 936 of 1,923 invitees (48.7%) returned the FOB-Gold and 940 of 1,923 invitees (48.9%) returned the OC-Sensor, a difference of -0.2% (confidence interval, -3.4% to 3.0%), well within the pre-specified 5% noninferiority margin (P = 0.001). Only one FOB-Gold (0.1%) and 4 OC-Sensors (0.4%) were not analyzable (P = 0.18). CONCLUSIONS: Although FOB-Gold was significantly but marginally considered easier to use than OC-Sensor, the number of analyzable tests and the participation rates in organized CRC screening are not affected when either of the FITs is implemented as a primary screening test.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".