<p>Trends in Glaucoma Filtration Procedures: A Retrospective Administrative Health Records Analysis Over a 13-Year Period in Canada</p>
Bibliographic record
Abstract
BACKGROUND: 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".