Pilot Validation Study: Canadian Global Rating Scale for Colonoscopy Services
Bibliographic record
Abstract
Background. The United Kingdom Global Rating Scale (GRS-UK) measures unit-level quality metrics processes in digestive endoscopy. We evaluated the psychometric properties of its Canadian version (GRS-C), endorsed by the Canadian Association of Gastroenterology (CAG). Methods. Prospective data collection at three Canadian endoscopy units assessed GRS-C validity, reliability, and responsiveness to change according to responses provided by physicians, endoscopy nurses, and administrative personnel. These responses were compared to national CAG endoscopic quality guidelines and GRS-UK statements. Results. Most respondents identified the overarching theme each GRS-C item targeted, confirming face validity. Content validity was suggested as 18 out of 23 key CAG endoscopic quality indicators (78%, 95% CI: 56–93%) were addressed in the GRS-C; statements not included pertained to educational programs and competency monitoring. Concordance ranged 75–100% comparing GRS-C and GRS-UK ratings. Test-retest reliability Kappa scores ranged 0.60–0.83, while responsiveness to change scores at 6 months after intervention implementations were greater ( P<0.001 ) in two out of three units. Conclusion. The GRS-C exhibits satisfactory metrics, supporting its use in a national quality initiative aimed at improving processes in endoscopy units. Data collection from more units and linking to actual patient outcomes are required to ensure that GRS-C implementation facilitates improved patient care.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".