Geographic Variation of Endoscopic Sinus Surgery in Canada
Bibliographic record
Abstract
OBJECTIVE: With an estimated 10,000 to 15,000 endoscopic sinus surgery (ESS) cases performed in Canada each year, identifying potential unwarranted practice patterns is important. The objective of this study is to examine the rates and geographic variation of ESS in the province of Alberta, Canada. STUDY DESIGN: Small area variation analysis. SETTING: Province of Alberta, Canada. SUBJECTS AND METHODS: The National Ambulatory Care Reporting System database was searched to identify all patients who received ESS between April 1, 2010, and March 31, 2013, in Alberta, Canada. The annual adjusted rates of ESS per 1000 people were calculated for each Alberta health zone and health status area. Geographic variations were evaluated with the extremal quotient, weighted coefficient of variation, and systematic component of variance. Chi-squared-test was used to quantify the significance of variation of the adjusted ESS rates across regions. RESULTS: The annual adjusted rate of ESS was 0.33 per 1000 people in Alberta, Canada. The mean extremal quotient for health status areas was 6.9, indicating a 7-fold difference between the highest and lowest regions. The mean coefficient of variation was 41.0, and the mean systematic component of variance was 10.5, which demonstrates "very high" variation. CONCLUSION: This study observed very high geographic variation in the rates of ESS across the province of Alberta. Given the negative impact of unwarranted surgical variation on quality of care, outcomes from this study indicate a need to further evaluate the delivery of care for ESS in Canada to improve overall health system performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| 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".