Will Women Diagnosed with Breast Cancer Provide Biological Samples for Research Purposes?
Bibliographic record
Abstract
BACKGROUND: Little is known about the response rates for biological sample donation and attitudes towards control recruitment, especially in younger women. The goals of this pilot study were to determine in women recently diagnosed with breast cancer, the proportion of cases willing to provide biological samples and for purposes of control recruitment, contact information for friends or colleagues. METHODS: A population-based sample of breast cancer cases (n = 417, 25-74 years) was recruited from the Ontario Cancer Registry in 2010 and self-administered questionnaires were completed to determine willingness to provide samples (spot or 24-hr urine, saliva, blood) and contact information for friends/colleagues for control recruitment. Using Χ2 analyses of contingency tables we evaluated if these proportions varied by age group (<45 and 45+) and other factors such as ethnicity, education, income, body mass index (BMI), smoking status and alcohol consumption. RESULTS: Cases were willing to provide blood samples, by visiting a clinic (62%) or by having a nurse visit the home (61%). Moreover, they would provide saliva (73%), and morning or 24-hr urine samples (66% and 52%). Younger cases (≤45) were 3 times (OR) more likely more than older cases to agree to collect morning urine (95% CI: 1.15-8.35). Only 26% of cases indicated they would provide contact information of friends or work colleagues to act as controls. Educated cases were more likely to agree to provide samples, and cases who consumed alcohol were more willing to provide contact information. Ethnicity, income, BMI and smoking had little effect on response rates. CONCLUSIONS: Reasonable response rates for biological sample collection should be expected in future case controls studies in younger women, but other methods of control selection must be devised.
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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 | 0.018 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".