O-90 Comparing responses from proxy and self-respondents in a population-based case-control study of occupational exposures and prostate cancer
Bibliographic record
Abstract
<h3>Introduction</h3> Using proxy respondents can improve response rates and reduce potential selection bias. However, possible differences in reporting by type of respondent have rarely been documented. We compared general and occupational information collected from proxies and self-respondents, including the interview duration and quality, number of jobs reported and the extent of missing data. <h3>Methods</h3> Data from the Prostate Cancer & Environment Study, a population-based case-control study conducted in 2005–2012 in Montreal, Canada were used. General information and detailed descriptions of each job held by male subjects, aged 65 years on average, were elicited during face-to-face interviews. Linear regression estimated the association between respondent status (proxy/self) and interview duration, adjusting for age and interviewer. Poisson regression was used to examine the relationship with number of reported jobs, adjusted for age, interviewer and career length. <h3>Results</h3> Analyses included 3,790 self-respondents and 135 proxy respondents; 72% of proxies were spouses. Proxies more often responded on behalf of blue-collar-workers. Interview duration for proxies was on average 25.1 minutes shorter than for self-respondents (95% confidence interval (CI) = -29.7; -20.5), with a difference more pronounced for blue-collar workers. Interview quality was judged by interviewers as doubtful/poor for 11% of proxies and 5% of self-respondents. Proxies reported 1.5 fewer jobs than self-respondents (95%CI = -1.8; -1.2), similarly for blue- and white-collar workers. The proportion of subjects who provided no details on work schedules, chemical exposures, use of protective equipment or workplace characteristics was higher among proxies than self-respondents (7% vs 4%). There were only marginal differences in reporting between proxies and self-respondents according to the case or control status of index subjects. <h3>Conclusion</h3> Findings suggest that the quantity and quality of occupational information elicited from proxies may be inferior to that of self-respondents, but that differences in reporting are non-differential according to disease status.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".