Geographic variation of endoscopic sinus surgery in the united states
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: The objective of this study was to examine the rates and geographic variation of endoscopic sinus surgery (ESS) in a representative sample of the US working population. STUDY DESIGN: Observational cohort study using the MarketScan Commercial Claim and Encounters database. METHODS: All patients who received ESS between 2009 and 2013 were included. The annual adjusted rates of ESS per 1,000 people were calculated for each US state. Geographic variations were evaluated using the extremal quotient (EQ), weighted coefficient of variation (CV), systematic component of variance (SCV), and empirical Bayes statistic. The χ(2) statistic tests was used to quantify variation of the adjusted ESS rates across states within the US. RESULTS: The annual adjusted rate of ESS was 0.94 per 1,000 people in the US. South Dakota and Alabama were observed to have the highest rates of ESS, 1.80 and 1.69, respectively. Vermont and Arkansas were observed to have the lowest rates of ESS, 0.51 and 0.57, respectively. The mean EQ was 4.54, indicating a four- to fivefold difference between the highest (South Dakota) and lowest (Vermont) states. The mean CV was 31.4 and mean SCV was 10.1, which demonstrates very high variation. CONCLUSIONS: This study observed very high geographic variation in the rates of ESS across the United States. Given that practice variation indicates the presence of potentially harmful and inefficient unwarranted care, outcomes from this study indicate a need to further evaluate the delivery of ESS to improve overall health system performance. LEVEL OF EVIDENCE: 2b.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".