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Record W7098702860

A STATED-CHOICE SURVEY

2015· article· en· W7098702860 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Materials Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsTest (biology)ColonoscopyPopulationColorectal cancer screeningBivariate analysisAffect (linguistics)Cancer screeningScreening test
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.250
GPT teacher head0.283
Teacher spread0.033 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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