OP171 Canadian Disease Registry Inventory: Environmental Scan Of The Literature
Bibliographic record
Abstract
Introduction In consideration of the lessons learned from other jurisdictions and other ongoing work in the disease registry data space, an opportunity existed to investigate the current Canadian landscape and identify opportunities for a Canadian registry list. Previously, no national-level inventory of registries existed in Canada that could provide the necessary information to support awareness and use of available data for decision-making. Methods A literature search was conducted on key resources, including MEDLINE and a focused internet scan. No methodological filters were applied to limit retrieval by publication type. The search was limited to documents published in English or French. Results Core characteristics of the identified registries were extracted and contextual information on the current landscape of disease registries in Canada was explored. A literature review and draft inventory list has been produced. Conclusions A CADTH environmental scan was undertaken to collect and report on existing Canadian disease registries and to identify key features, characteristics, and intersections. This information and analysis increase the potential of Canadian registries to inform decision-making and identifies opportunities for the optimal use of registry data in Canada more broadly.
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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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.070 | 0.119 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".