Linkage of Clinical Trial and Administrative Data: A Survey of Cancer Patient Preferences
Bibliographic record
Abstract
Background: Personal health information, including diagnoses and hospital admissions, is routinely collected in administrative databases. Patients enrolling on clinical trials consent to separate collection and storage of their personal health information. We evaluated patient preferences for linking long-term data from administrative databases with clinical trials. Methods: Adults with cancer attending outpatient clinics at 3 Ontario hospitals were surveyed about their willingness, when faced with the hypothetical scenario of participating in a clinical trial, to provide potentially identifying information such as initials and date of birth to facilitate long-term research access to normally deidentified publicly collected databases. Results: Of 569 patients surveyed, 335 (59%) were women, 452 (79%) were white, 385 (68%) had a post-secondary education, and 386 (68%) had never participated in a clinical trial. Median age in the group was 59 years. Most participants (93%, cohort 1) would allow long-term access to their information and allow personal information to be used to match clinical trial with administrative data. At the time of clinical trial closure, two thirds of participants (68%, cohort 2) preferred to make additional clinical information available through linkage with administrative databases, and 8 (9%) preferred to have no further information made available to researchers. No significant differences were found in the subset of patients who were part of a clinical trial and those who had never participated (p = 0.65). Interpretation: Almost all patients would allow a clinical trial research team to access their confidential information, providing a more comprehensive assessment of an intervention’s long-term risks and benefits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".