A44 MEASURING COLONOSCOPY QUALITY AT THE POPULATION LEVEL IN ONTARIO, CANADA
Bibliographic record
Abstract
The importance of high quality colonoscopy is widely recognized, however, it is challenging to measure its quality for an entire population. The aim of this study is to measure on 9 key colonoscopy quality indicators for all practicing endoscopists in Ontario, Canada in 2015. Using linked health administrative databases, colonoscopies performed between Jan. 1 and Dec. 31 2015 were identified. Endoscopists were defined as those who had performed ≥6 colonoscopies in this period. We measured 9 quality indicators at the endoscopist level: annual colonoscopy volume, polypectomy rate, cecal intubation rate, polypectomy-associated bleeding, perforations, colorectal cancers (CRC) detected, post-colonoscopy CRCs (PCCRC), poor bowel preparation rate and colonoscopies with recent normal findings (% colonoscopies in 2015 where a 2nd normal and complete colonoscopy was done in the prior 3 years). Provincial rates and median endoscopist values were reported. For less frequent events, the proportion of endoscopists is reported by number of events. In 2015, 921 endoscopists performed 464,506 colonoscopies in Ontario. Among the endoscopists, 20% were women; 57% were surgeons, 33% were gastroenterologists and 10% were internists or other practitioners. 64% practiced in hospitals, 11% practiced in private clinics and 25% practiced in both settings. See Tables 1 & 2 for quality indicators. We have found that Ontario endoscopists performed well in 2015, although there is still room for improvement. These indicators will be used in centralized, province-wide provider performance reporting with the goal of improving colonoscopy quality across the province. Table 1: Provincial rates and median endoscopist values for colonoscopy quality indicators a=Unable to calculate provincial rate as volume is calculated per endoscopist; b=Events infrequent, therefore median is zero. See table 2. Canadian Cancer Society Research Institute
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".