An Update on the Development and Feasibility Assessment of Canadian Quality Indicators for Atrial Fibrillation and Atrial Flutter
Bibliographic record
Abstract
BACKGROUND: In 2010, the Canadian Cardiovascular Society Atrial Fibrillation/Atrial Flutter (AF/AFL) quality indicator (QI) working group was established to develop QIs and assess feasibility of measurement. After extensive review, 3 priority QIs were selected. However, none were measurable at a national level. METHODS: The working group reconvened in 2017 to review the relevance of previously proposed QIs, identify opportunities to develop new QIs, and propose an initial strategy for measuring and reporting. RESULTS: Two additional priority QIs were added to the previous 3: proportion of patients with nonvalvular (NV) AF/AFL sorted by stroke risk stratum and annual rate of hospitalization for a new heart failure diagnosis. An environmental scan was undertaken to determine the potential of existing databases to provide national and provincial estimates. On the basis of validated administrative codes, the Canadian Institute for Health Information discharge abstract database can be used for inpatients. In collaboration with the Canadian Primary Care Sentinel Surveillance Network, 2 of the 5 QIs can be assessed in outpatients (patients with NVAF/AFL sorted by stroke risk stratum and high risk for stroke NVAF/AFL receiving oral anticoagulation). Stroke prevention therapy can be further measured in selected provinces with linked databases including prescriptions. CONCLUSIONS: This first step could provide a better initial understanding of the quality of AF/AFL care in Canada, but important gaps in the meaningful measurement of QIs remain. The AF/AFL QI working group has limited capacity to make progress without national level leadership and the resources to support data aggregation, data analysis, and pan-Canadian reporting.
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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 | 0.050 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".