Effects of Workload on Return to Work After Elective Lumbar Spine Surgery
Bibliographic record
Abstract
Study Design Retrospective analysis was performed of a multi-center Canadian Spine Outcomes and Research Network (CSORN) surgical database. Objective To determine the rate and time to return to work (RTW) based on workload intensity after elective degenerative lumbar spine surgery. Methods Patients working pre-operatively, aged greater than 18, who underwent a primary one- or two-level elective lumbar spine surgery for degenerative conditions between January 2015 and October 2020 were evaluated. The percentage of patients who returned to work at 1 year and the time to RTW post-operatively were analyzed based on workload intensity. Results Of the 1290 patients included in the analysis, the overall rate of RTW was 82% at 1 year. Based on workload there was no significant difference in time to RTW after a fusion procedure, with median time to RTW being 10 weeks. For non-fusion procedure, the sedentary group had a statistically significantly quicker time to RTW than the light-moderate ( P < .005) and heavy-very heavy (<.027) groups. Conclusions The rate of RTW ranged between 84% for patients with sedentary work to 77% for patient with a heavy-very heavy workload. Median time to resumption of work was about 10 weeks following a fusion regardless of work intensity. There was more variability following non-fusion surgeries such as laminectomy and discectomy reflecting the patient’s job demands.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".