Provincial Variation of Cochlear Implantation Surgical Volumes and Cost in Canada
Bibliographic record
Abstract
Objectives To investigate provincial cochlear implantation (CI) annual volume and cost trends. Study Design Database analysis. Setting National surgical volume and cost database. Subjects and Methods Aggregate‐level provincial CI volumes and cost data for adult and pediatric CI surgery from 2005 to 2014 were obtained from the Canadian Institute for Health Information. Population‐level aging forecast estimates were obtained from the Ontario Ministry of Finance and Statistics Canada. Linear fit, analysis of variance, and Tukey’s analyses were utilized to compare variances and means. Results The national volume of annual CI procedures is forecasted to increase by <30 per year (R2 = 0.88). Ontario has the highest mean annual CI volume (282; 95% confidence interval, 258‐308), followed by Alberta (92.0; 95% confidence interval, 66.3‐118), which are significantly higher than all other provinces (P <. 05 for each). Ontario’s annual CI procedure volume is forecasted to increase by <11 per year (R2 = 0.62). Newfoundland and Nova Scotia have the highest CI procedures per 100,000 residents as compared with all other provinces (P <. 05). Alberta, Newfoundland, and Manitoba have the highest estimated implantation cost of all provinces (P <. 05). Conclusions Historical trends of CI forecast modest national volume growth. Potential bottlenecks include provincial funding and access to surgical expertise. The proportion of older adult patients who may benefit from a CI will rise, and there may be insufficient capacity to meet this need. Delayed access to CI for pediatric patients is also a concern, given recent reports of long wait times for CI surgery.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".