Utilization and Outcomes for Spine Surgery in the United States and Canada
Bibliographic record
Abstract
STUDY DESIGN: A retrospective cohort study. OBJECTIVE: The aim of this study was to examine variation in spine surgery utilization between the province of Ontario and state of New York among all patients and pre-specified patient subgroups. SUMMARY OF BACKGROUND DATA: Spine surgery is common and costly. Within-country variation in utilization is well studied, but there has been little exploration of variation in spine surgery utilization between countries. METHODS: We used population-level administrative data from Ontario (years 2011-2015) and New York (2011-2014) to identify all adults who underwent inpatient spinal decompression or fusion surgery using relevant procedure codes. Patients were stratified according to age and surgical urgency (elective vs. emergent). We calculated standardized utilization rates (procedures per-10,000 population per year) for each jurisdiction. We compared Ontario and New York with respect to patient demographics and the percentage of hospitals performing spine surgery. We compared utilization rates of spinal decompression and fusion surgery in Ontario and New York among all patients and after stratifying by surgical urgency and patient age. RESULTS: Patients in Ontario were older than patients in New York for both decompression (mean age 58.8 vs. 51.3 years; P < 0.001) and fusion (58.1 vs. 54.9; P < 0.001). A smaller percentage of hospitals in Ontario than New York performed decompression (26.1% vs. 54.9%; P < 0.001) or fusion (15.2% vs. 56.7%; P < 0.001). Overall, utilization of spine surgery (decompression plus fusion) in Ontario was 6.6 procedures per-10,000 population per-year and in New York was 16.5 per-10,000 per-year (P < 0.001). Ontario-New York differences in utilization were smaller for emergent cases (2.0 per 10,000 in Ontario vs. 2.5 in New York; P < 0.001), but larger for elective cases (4.6 vs. 13.9; P < 0.001). The lower utilization in Ontario was particularly large among younger patients (age <60 years). CONCLUSION: We found significantly lower utilization of spine surgery in Ontario than in New York. These differences should inform policy reforms in both jurisdictions. LEVEL OF EVIDENCE: 3.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".