WILL WOMEN DIAGNOSED WITH BREAST CANCER PROVIDE BIOLOGICAL SAMPLES FOR FUTURE RESEARCH PURPOSES?
Bibliographic record
Abstract
Background and Aims: Biological samples are often used to assess exposures in case control studies of cancer risk. Little is known about the response rates for biological sample provision, especially in younger age groups, and how these are affected by other demographic or lifestyle variables. A population based study of breast cancer patients was conducted to estimate these rates and to obtain information on the recruitment of friend/colleague controls. Methods: Breast cancer cases diagnosed April-May 2010 were recruited to determine the proportion willing to provide biological samples (morning or 24 hour urine, saliva, blood) and contact information for similarly aged women for control recruitment. Cases (n=417) were women 25-74 years of age identified through the Ontario Cancer Registry and 67% (n=278) of those completed a mailed questionnaire. Results: Women indicated they were willing to provide blood samples, by visiting a clinic (62%) or having a nurse visit the home (61%). They also were willing to provide saliva samples (73%) and morning (66%) or 24-hr urine samples (52%). Compared to older women, younger women (•45) were consistently more likely to agree to provide any biological samples and were 3.1 times (OR) more likely to report they would collect a morning urine sample (95% CL: 1.15-8.35). Only 26% of cases indicated they would be willing to give contact information for similarly aged women (controls) to provide samples in future studies. Educated women were more likely to agree to provide biological samples, and women who consumed alcohol were more likely to agree to provide contact information than non-drinkers. Ethnicity, income, body mass index, and smoking status did not influence these results. Conclusions: Reasonable response rates for biological sample collection can be expected in future case controls studies of breast cancer (especially among younger women), however, 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 | 0.031 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".