Relationship between spinal magnetic resonance imaging findings and candidacy for spinal surgery.
Bibliographic record
Abstract
OBJECTIVE: To compare the prevalence of spinal abnormalities found on magnetic resonance imaging (MRI) in symptomatic surgical candidates and non-surgical patients. DESIGN: Retrospective cohort study. SETTING: A single academic spine surgery practice in Toronto, Ont. PARTICIPANTS: A total of 1586 symptomatic patients referred during a 32-month period; based on chart review, patients were classified as surgical candidates (n=722), non-surgical patients (n=690), or indeterminate regarding surgical candidacy (n=174). MAIN OUTCOME MEASURES: Prevalence rates of different spinal abnormalities between the 2 cohorts, including type, severity, and number of levels of abnormalities detected on lumbar MRI. RESULTS: The total number of abnormalities did not differ between the 2 groups (P=.26). The non-surgical group exhibited more degenerative disk disease (P<.01), while surgical candidates had a higher prevalence of spinal stenosis and spondylolisthesis (P<.01). In multivariate analysis, age (adjusted odds ratio [AOR] per 10-year increase 3.33, 95% confidence interval [CI] 3.32 to 3.33), disk herniation (AOR 1.49, 95% CI 1.16 to 1.89), spinal stenosis (AOR 1.61, 95% CI 1.26 to 2.05), and spondylolisthesis (AOR 2.83, 95% CI 2.08 to 3.88) were independent predictors of surgical candidacy. CONCLUSION: These results might enable physicians without specialty training in spinal disorders to more effectively use MRI reports when deciding on referral to surgical or non-surgical specialists. In jurisdictions with long wait times for elective spinal surgery consultation, a more directed referral is one of many steps necessary to improve patient access and management.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".