A Retrospective Cohort Study of Healthcare Utilization Associated with Paravertebral Blocks for Chronic Pain Management in Ontario
Bibliographic record
Abstract
Background: Injections, particularly paravertebral blocks (PVBs), are frequently performed procedures in Ontario, Canada, for the management of chronic pain, despite limited evidence and risk of complications. Aim: This study examines usage patterns of PVBs to evaluate their effects on healthcare utilization and opioid prescribing. Methods: A retrospective cohort study in Ontario using administrative data. Ontario residents receiving their initial PVBs between July 1, 2013 and March 31, 2018 were included. Changes in use of other interventions, physician visits, and opioids were compared to the 12-month periods before and after index PVBs. Data use was authorized under section 45 of Ontario’s Personal Health Information Protection Act. Results: 47,723 patients received their initial PVBs in the study period. The rate of index PVBs increased from 1.61 per 10,000 population (2013) to 2.26 per 10,000 (2018). Initial PVBs were performed most commonly by family physicians (N = 25,042), followed by anesthesiologists (N = 14,195). 23,386 patients (49%) received 1 to 9 repeat PVBs in the 12 months after index PVB; 12,474 patients (26.15%) received 10 or more. Use of other nonimage guided interventional pain procedures per patient (mean±SD) increased from 2.19 ± 9.35 to 31.68 ± 52.26 in the year before and after index PVB. Relevant physician visits per patient (mean±SD) also increased from 2.92 ± 3.61 to 9.64 ± 11.77. Mean opioid dosing did not change significantly between the year before and the year after index PVB. Conclusion: PVBs are associated with increases in healthcare utilization and no change in opioid use patterns.
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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 | 0.000 |
| 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".