Defining Quality Indicators for Best-Practice Management of Inflammatory Bowel Disease in Canada
Bibliographic record
Abstract
BACKGROUND: There is a paucity of published data regarding the quality of care of inflammatory bowel disease (IBD) in Canada. Clinical quality indicators are quantitative end points used to guide, monitor and improve the quality of patient care. In Canada, where universal health care can vary significantly among provinces, quality indicators can be used to identify potential gaps in the delivery of IBD care and standardize the approach to interprovincial management. METHODS: The Emerging Practice in IBD Collaborative (EPIC) group generated a shortlist of IBD quality indicators based on a comprehensive literature review. An iterative voting process was used to select quality indicators to take forward. In a face-to-face meeting with the EPIC group, available evidence to support each quality indicator was presented by the EPIC member aligned to it, followed by group discussion to agree on the wording of the statements. The selected quality indicators were then ratified in a final vote by all EPIC members. RESULTS: Eleven quality indicators for the management of IBD within the single-payer health care system of Canada were developed. These focus on accurate diagnosis, appropriate and timely management, disease monitoring, and prevention or treatment of complications of IBD or its therapy. CONCLUSIONS: These quality indicators are measurable, reflective of the evidence base and expert opinion, and define a standard of care that is at least a minimum that should be expected for IBD management in Canada. The next steps for the EPIC group involve conducting research to assess current practice across Canada as it pertains to these quality indicators and to measure the impact of each of these indicators on patient outcomes.
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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 | 0.113 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.024 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".