Potential triaging of referrals for lumbar spinal surgery consultation: a comparison of referral accuracy from pain specialists, findings from advanced imaging and a 3-item questionnaire.
Bibliographic record
Abstract
BACKGROUND: Waiting times to see a spinal surgeon are among the highest in Canada. However, most patients who are referred would not benefit from surgical care. Effective triaging of surgical candidates may reduce morbidity related to prolonged waiting times and optimize use of limited resources. METHODS: We administered a questionnaire consisting of 3 items identifying leg-dominant or back-dominant pain among 119 consecutive patients who presented at a community spinal pain centre or a spinal surgical unit for assessment of an elective lumbar problem. We analyzed the questionnaire under 2 different scenarios: 1 hypothesized to be more sensitive and 1 hypothesized to be more specific. RESULTS: For the "sensitive" scenario of clearly back-dominant pain, the sensitivity of the questionnaire was 100% in identifying appropriate surgical candidates. For the "specific" scenario of leg-dominant pain, the questionnaire had a sensitivity of 83% and specificity of 73% in identifying appropriate surgical candidates, which was significantly superior to findings on computed tomography or magnetic resonance imaging (i.e., presence of neurocompressive lesions). When comparing the accuracy of the questionnaire in identifying appropriate surgical candidates to that of an assessment performed by a pain specialist at an acute spinal pain clinic, we found no statistically significant differences between the 2 methods. CONCLUSION: Use of the questionnaire when triaging patients may decrease the number of unnecessary referrals to spine surgeons. Adopting such a method of triaging could reduce waiting times for appropriate surgical candidates and potentially improve the outcomes of any resulting spinal surgery performed in a timely fashion.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".