Linking trial data to ICES: incorporating a prompt in a research ethics protocol submission platform in Southwestern Ontario
Bibliographic record
Abstract
IntroductionThe linkage of trial data with large administrative databases can enable long-term passive follow-up of participants at a significantly lower cost than direct participant follow-up in a study. Nonetheless, many researchers are unaware of this opportunity and/or the regulatory requirements to facilitate linkage. ApproachIn 2017, Western University Canada’s Office of Human Research Ethics (OHRE) incorporated a prompt into its online protocol submission platform, asking researchers if they have considered linking their trial data with ICES. ICES is a not-for-profit research and analytics institute in Ontario, Canada with a repository of over 100 data holdings comprised of record-level, coded and linkable health and health-related data. If a researcher selects ‘yes’ to this prompt, they are provided with additional information about ICES, identifiers required for linkage, and language to be included in letters of consent. ResultsThe incorporation of a prompt into the ethics protocol submission platform allowed the OHRE to identify protocols interested in linking trial data with ICES. Over the past 5 years, an average of 2.6% of protocols submitted to the Health Sciences Research Ethics Board included an intent to link data with ICES. The OHRE verified that these protocols had carefully considered regulatory requirements, which helped to streamline the ethical, privacy, and legal processes required for linkage. ConclusionIncorporating a prompt into ethics protocol submission platforms can help to streamline regulatory processes and promote awareness about opportunities to link trial data with large administrative databases.
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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 | 0.132 | 0.169 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".