Use of Screening Tests, Diagnosis Wait Times, and Wait-Related Satisfaction in Breast and Prostate Cancer
Bibliographic record
Abstract
BACKGROUND: Understanding factors relating to the perception of wait time by patients is key to improving the patient experience. METHODS: We surveyed 122 breast and 90 prostate cancer patients presenting at clinics or listed on the cancer registry in Newfoundland and Labrador and reviewed their charts. We compared the wait time (first visit to diagnosis) and the wait-related satisfaction for breast and prostate cancer patients who received regular screening tests and whose cancer was screening test-detected ("screen/screen"); who received regular screening tests and whose cancer was symptomatic ("screen/symptomatic"); who did not receive regular screening tests and whose cancer was screen test-detected ("no screen/screen"); and who did not receive regular screening tests and whose cancer was symptomatic ("no screen/symptomatic"). RESULTS: Although there were no group differences with respect to having a long wait (greater than the median of 47.5 days) for breast cancer patients (47.8% screen/screen, 54.7% screen/symptomatic, 50.0% no screen/ screen, 40.0% no screen/symptomatic; p = 0.814), a smaller proportion of the screen/symptomatic patients were satisfied with their wait (72.5% screen/ screen, 56.4% screen/symptomatic, 100% no screen/ screen, 90.9% no screen/symptomatic; p = 0.048). A larger proportion of screen/symptomatic prostate cancer patients had long waits (>104.5 days: 41.3% screen/screen, 92.0% screen/symptomatic, 46.0% no screen/screen, 40.0% no screen/symptomatic; p = 0.011) and a smaller proportion of screen/ symptomatic patients were satisfied with their wait (71.2% screen/screen, 30.8% screen/symptomatic, 76.9% no screen/screen, 90.9% no screen/symptomatic; p = 0.008). CONCLUSIONS: Diagnosis-related wait times and satisfaction were poorest among patients who received regular screening tests but whose cancer was not detected by those tests.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".