Assessing feasibility and sex-related inequity in the cardiac rehabilitation quality indicators in Manitoba
Bibliographic record
Abstract
The cardiac rehabilitation quality indicators (CRQIs) developed by the Canadian Cardiovascular Society provide a means to standardize program assessment and identify sex-related inequities. No formal evaluation of the CRQIs has been conducted in Manitoba. An environmental scan for the CRQIs was performed using data in the electronic medical record at two cardiac rehabilitation (CR) sites in Winnipeg for 2016-2019 referrals. Of the 8116 referrals, 7758 (5491 males and 2267 females) had geographical access and were eligible for CR. The Manitoba Centre for Health Policy Data Quality Framework informed the data quality assessment. Thirteen CRQIs were available; four were considered high quality; nine demonstrated moderate to significant missing data. In addition to missing values, potential misclassification of risk (CR-4) and physiologically implausible and invalid dates were assessed and identified (CR-13 and CR-17). Each site had a physician medical director (CR-31) and a documented emergency response strategy (CR-32). Only high-quality data were evaluated for sex-related differences using chi-square and median tests. Women had lower enrollment (CR-3), and more women enrolled after the median of 41 days (CR-2b). Engagement with CR partners, including frontline staff, and utilizing strategies to assess and limit physiologically implausible values and dates will enhance data capture and quality.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".