Bibliographic record
Abstract
Background: Patient preferences can affect colorectal cancer (CRC) screening test use. We compared utility-based preferences for alternative CRC screening tests from a stated-preference discrete-choice survey of the general population and physicians in Canada and the United States. Methods: General population respondents (Canada, n5 501; US, n5 1087) participated in a survey with 12 choice scenarios and 9 CRC screening test attributes. Physicians (n5 100, both Canada and US) reported expected patient preferences. We estimated relative importance of attributes using bivariate probit regression analysis and calculated willingness-to-pay for various CRC screening tests. Results: In 28 and 31 % of scenarios, Canadian and US respondents, respectively, chose no screening over a hypothetical test. Canadian (45%) and US (46%) physicians expected patients to choose no screening more often. For all groups the most important attribute was sensitivity, but physicians ’ perception of patients ’ preferences are significantly different from actual preferences. Other key attributes are those related to test performance or the testing process. Fecal DNA, colonoscopy, and virtual colonoscopy were the most preferred tests by all groups, but respondents were willing-to-pay more than physicians predicted.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".