Patient assessment of tests to detect cervical cancer
Bibliographic record
Abstract
OBJECTIVES: This study was undertaken to understand how women view characteristics of tests for cervical dysplasia, because these characteristics can affect patient decision-making about screening and follow-up. METHODS: We recruited women who participated in a clinical trial of optical spectroscopy for the diagnosis of cervical dysplasia and used conjoint analysis to assess the women's preferences concerning test attributes. One group of women had a history of an abnormal Papanicolaou smear (diagnostic sample), while the other group did not (screening sample). Participants rated pairs of test scenarios that varied on characteristics such as test sensitivity and painfulness. Based on their responses, the relative importance of test sensitivity, specificity, timing of results feedback and treatment, and pain were calculated, and a cluster analysis was done to identify subgroups of participants with different preference patterns. RESULTS: In the overall sample, sensitivity was the most important attribute, followed by timing, specificity, and pain. Cluster analysis revealed four distinct groups who placed varying importance on each characteristic. The participants in the cluster for which pain was the most important attribute were more likely to be diagnostic patients, non-white, and have low education levels. They also reported more anxiety and pain during the examination than participants in other clusters. CONCLUSIONS: To continue to reduce morbidity and mortality from cervical cancer, developers of new testing procedures should take into account test attributes such as these, which may affect adherence to screening and diagnostic follow-up to further minimize morbidity and mortality from cervical cancer.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".