Establishing Funding Rates for Colonoscopy and Gastroscopy Procedures in Ontario
Bibliographic record
Abstract
Introduction: This paper describes the funding rates established in Ontario to reflect best practices in hospital-based care delivery for these endoscopic procedures: colonoscopy, colonoscopy biopsy, gastroscopy, gastroscopy biopsy, and colonoscopy combined with gastroscopy. Methods: The funding rates are based on direct costs and were established using a micro-costing approach after receipt of inputs from 3 working groups and a review of the administrative data and literature, where applicable. The first group advised on nursing activities, time, and staffing ratios along the patient pathway for each of the procedures. The second group provided recommendations about the duration for each procedure, and the third group provided information about supplies and equipment, their use, and costs. Results: The resulting funding rates are $161.18 for colonoscopy and $151.08 for gastroscopy (without accompanying interventions), $16.06 for colonoscopy biopsy and $8.22 for gastroscopy biopsy (added to the respective procedures), and $207.26 for combined colonoscopy and gastroscopy. Detailed costs for each component embedded in the rates are also provided. Conclusions: The rates came into effect in April 2018. The process and outcomes described here allowed for a transparent pricing mechanism in which funding follows the patient, clinical expert consensus is the basis for practice, and providers and payers both understand the components.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".