Expanding the impact of a longstanding Canadian cardiac registry through data linkage: challenges and opportunities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) began as a province-wide inception cohort of all adult Alberta residents undergoing cardiac catheterization for ischemic heart disease. Strengths of the APPROACH initiative include the prospective collection of detailed clinical, procedural, and treatment information, measured at point-of-care. While this aspect of APPROACH provides data users with several advantages over use of typical administrative data, the ability to link APPROACH with data from multiple other sources has provided several unique opportunities to measure cardiovascular care and outcomes. As of June 2018, clinical information has been collected by APPROACH on over 240,000 adult Alberta residents. Linkage of this rich clinical data to administrative health data (eg. Vital statistics, hospitalizations, ambulatory events, prescription medications), secondary use clinical data (e.g. laboratory, ECG, rehabilitation, EMR, imaging) and other data sources (eg. Geospatial, crime data, meteorological) allows better study of the determinants of a patient's health trajectory. This paper describes applied examples of work that has leveraged the potential of linking several datasets with the APPROACH registry.
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.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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 it