Retrospective cohort study of healthcare utilization and opioid use following radiofrequency ablation for chronic axial spine pain in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Radiofrequency ablation (RFA) is a common treatment modality for chronic axial spine pain. Controversy exists over its effectiveness, and outcomes in a real-world setting have not been evaluated despite increasing use of RFA. This study examined changes in healthcare utilization and opioid use after RFA in Ontario, Canada. METHODS: This retrospective cohort study was conducted in Ontario using administrative data. Ontario residents receiving their initial RFA between 1 January 2009 and 31 March 2015 were included. Physician visits, spinal injections, and opioid dosing/prescriptions in the 12-month periods before and after RFA were compared. RESULTS: The study included 4653 patients. The number of RFA procedures significantly increased from 2009 to 2014 (22.5 cases/1 000 000 person-years to 82.5 cases/1 000 000 person-years). 4465 patients had at least one physician visit pre-RFA; there was a significant 23.89% reduction in physician visits post-RFA (pre-RFA: 29 616 visits; post-RFA: 22 542 visits). All reviewed specialties demonstrated a decrease in physician visits post-RF except neurosurgery. 3445 (85.70%) fewer spinal interventions for axial pain (medial/lateral branch blocks, facet/sacroiliac injections) were performed post-RFA. Significantly fewer epidurals were also performed post-RFA. 198 of 1007 patients (19.66%) on the Ontario Drug Benefit who received opioids pre-RFA did not require a postprocedure opioid prescription. Mean opioid dosing was unchanged post-RFA. CONCLUSIONS: Healthcare utilization was significantly reduced in the 12 months following RFA, and some patients eliminated opioid use. Selection criteria for RFA are not standardized in Ontario, and appropriate use guidelines for spine interventions may improve outcomes and reduce unnecessary procedures.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".