The Influence of Wait Time on Surgical Outcomes in Elective Lumbar Degenerative Spine Conditions: A Retrospective Multicentre Cohort Study
Bibliographic record
Abstract
Study DesignRetrospective cohort study.ObjectivesThe impact of delayed access to operative treatment on patient reported outcomes (PROs) for lumbar degenerative conditions remains unclear. The goal of this study is to evaluate the association between wait times for elective lumbar spine surgery and post-operative PROs.MethodsThis study is a retrospective analysis of patients surgically treated for a degenerative lumbar conditions. Wait times were calculated from primary care referral to surgery, termed the cumulative wait time (CWT). CWT benchmarks were created at 3, 6 and 12 months. A multivariable logistic regression model was used to measure the associations between CWT and meeting the minimally clinically important difference (MCID) for the Oswestry Disability Index (ODI) score at 12 months post-operatively.ResultsA total of 2281 patients were included in the study cohort. The average age was 59.4 years (SD 14.8). The median CWT was 43.1 weeks (IQR 17.8 - 60.6) and only 30.9% had treatment within 6 months. Patients were more likely achieve the MCID for the ODI at 12 months post-operatively if they had surgery within 6 months of referral from primary care (OR 1.22; 95% CI 1.11 - 1.34). This relationship was also found at a benchmark CWT time of 3 months (OR 1.33; 95% CI 1.15 - 1.54) though not at 12 months (OR 1.08; 95% CI 0.97 - 1.20).ConclusionsPatients who received operative treatment within a 3- and 6-month benchmark between referral and surgery were more likely to experience noticeable improvement in post-operative function.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".