Predictors of non-pharmacological, non-surgical treatment utilization prior to thoracolumbar spine surgery in Manitoba: A Canadian spine outcomes research network (CSORN) study.
Bibliographic record
Abstract
Current evidence for back pain management supports a stepwise approach to care; beginning with non-pharmacological, non-surgical treatment, progressing to pharmacotherapy, with surgery as a last resort. The present study utilized the Canadian Spine Outcomes Research Network (CSORN) data registry for Manitobans receiving elective spine surgery to understand current clinical practice patterns. The first objective was to determine the proportion of elective spine surgery patients who engage in non-pharmacological, non-surgical treatment prior to undergoing elective thoracolumbar spine surgery. The second objective was to investigate potential predictors for non-pharmacological, non-surgical treatment engagement amongst CSORN patients eligible for thoracolumbar spine surgery. The study utilized a retrospective cohort design among elective thoracolumbar spine surgery patients in Manitoba, Canada. Binary logistic regression was used to identify if: 1) patient characteristics; 2) pain and disability measures; 3) and frequency of prescription narcotic use predict engagement with non-pharmacological, non-surgical treatment prior to undergoing elective thoracolumbar spine surgery. The analysis revealed that 41.7% of CSORN patients from Manitoba reported minimal-to-no engagement with non-pharmacological, non-surgical treatment in the six-months prior to undergoing elective thoracolumbar spine surgery. The final logistic model revealed four statistically significant predictors: 1) 61-90 years of age (odds ratio [OR] 4.6, 95% confidence interval [CI] 2.0 – 10.7, p= .000); 2) Oswestry disability index score >60% (OR 3.5, 95% CI 1.4 – 9.2, p= .010; 3) BMI score 25 – 29.9 (OR 6.7 , 95% CI 2.2 – 20.9, p= .001) and BMI ≥30 (OR 4.2, 95% CI 1.4 – 12.2, p= .009); and 4) female biological sex (OR 2.4, 95% CI 1.0 – 5.6, p= .039) were significant independent predictors for minimal-to-no engagement with non-pharmacological, non-surgical treatment prior to undergoing elective thoracolumbar spine surgery. The study revealed that the stepwise approach to back pain management in Manitoba is not optimal. Concerted efforts are required to understand why biological females at birth, patients of older age, those that are overweight or obese, and perceive themselves as debilitated are less likely to engage in non-pharmacological, non-surgical treatment prior to elective thoracolumbar spine surgery in Manitoba, Canada. Further refinement of the CSORN data collected is required to better understand Manitoba’s spine pain population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".