Trends in Glaucoma Filtration Procedures: A Retrospective Administrative Health Records Analysis Over a 13-Year Period in Canada
Bibliographic record
Abstract
Vinay Kansal,1 James J Armstrong,2,3 Cindy ML Hutnik2– 4 1University of Saskatchewan, Department of Ophthalmology, Saskatoon, SK, Canada; 2Western University Canada, Schulich School of Medicine, Department of Pathology and Laboratory Medicine, London, ON, Canada; 3Western University Canada, Schulich School of Medicine, Department of Ophthalmology, London, ON, Canada; 4Ivey Eye Institute, St. Joseph’s Hospital, London, ON, CanadaCorrespondence: Cindy ML HutnikIvey Eye Institute, St. Joseph’s Hospital, 268 Grosvenor Street, London, ON N6A 4V2, CanadaTel +1 519-646-6100 Ext 66272Fax +1 519-646-6410Email cindy.hutnik@sjhc.london.on.caBackground: Glaucoma surgical management has evolved significantly with the introduction of minimally invasive glaucoma surgery. Our aim was to evaluate trends in Canadian glaucoma surgery billing code usage as a surrogate index of the current impact of this new technology in Canada’s publicly funded health-care system.Methods: Retrospective administrative health records analysis of all patients who underwent a publicly funded glaucoma filtration procedure from January 2003 to December 2016 in the 6 largest Canadian provinces. The frequency of glaucoma-related procedures was adjusted against primary open-angle glaucoma prevalence data. Frequency of all glaucoma filtration procedures with and without implantation of a drainage device in each province per year is reported.Results: Nationwide, glaucoma filtration procedures per 1000 primary open-angle glaucoma patients per year remained constant, with increased drainage device implantation over time (P< 0.0001). Ontario and Nova Scotia mirrored the overall population. British Columbia and Saskatchewan showed increased rates of glaucoma filtration surgery, with increased drainage device implantations. In Quebec, overall filtration surgery decreased, while the rate of device implantation increased (p< 0.0001). Alberta showed a decline in filtration surgery and device implantations from 2003 to 2008, and then increased thereafter.Conclusion: Over the study period, there was a distinct trend towards billing code usage for implanted devices. Challenges encountered during this investigation highlight the need for identifiers in provincial health databases to accommodate the introduction of novel technologies. The absence of specific billing codes for newer technologies prevents accurate analyses of impact, utilization, efficacy and cost implications in contemporary patient management.Keywords: glaucoma, minimally invasive glaucoma surgery, MIGS, glaucoma filtration surgery, population analysis
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".