Patient preferences for research access to administrative data in Ontario.
Bibliographic record
Abstract
144 Background: Clinical trials collect outcome data until the trial ends. Subsequent health data is often available in government or health care administrative databases, yet this information is often not made available to researchers. We assessed patient preferences of long-term linkage of such administrative databases to clinical trials databases. Methods: A self-reported questionnaire was administered to outpatients in clinics of academic and community cancer centres. Cancer patients were presented with a hypothetical scenario of a clinical trial and asked about their willingness to allow confidential research access to their personal health information contained in administrative databases. Results: Of 524 patients, 54% were females, median age was 60 (range 19–93), 81% were Caucasian, and 61% had post-secondary education. Of cancer sites, 20% had breast cancer, 17% GI, 14% GU, 13% hematology, 12% thoracic, 12% head/neck and 11% gynecologic cancers. One-third (33%) had participated in a clinical trial. An overwhelming 93% allowed confidential access to health information in administrative databases (59% always, 34% depending on circumstances) including access to initials and birth dates, to be used to match information across databases (70% always, 23% depending on circumstances). Only 3% were unwilling to release their information under any circumstances. When asked about other identifiable information, such as name and address, more than 74% allowed the secure storage of this information at the central study coordinating center. In a new cohort of 103 patients, 84% preferred long-term data to be continually available to researchers after the clinical trial has ended; only 9% did not want this option. Results were similar across all sociodemographic subgroups studied including patients who had prior participation in clinical trials, although older patients were even more likely to allow access than younger patients (p = 0.02). Conclusions: The vast majority of patients were willing to have their long-term outcome data collected through government and health care administrative databases made available to clinical trial researchers, to improve our understanding of long-term outcomes of trial procedures and drugs.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".