The Effect of Compensation Status on Waiting Time for Elective Surgical Lumbar Discectomy
Bibliographic record
Abstract
STUDY DESIGN: Prospective cohort study of patients registered for elective surgical lumbar discectomy (ESLD) between November 1999 and December 2003 at a major tertiary care center in Vancouver. OBJECTIVE: To determine whether compensation status was associated with longer waiting time for ESLD. SUMMARY OF BACKGROUND DATA: In Canada, access to publicly funded ESLD is managed through waitlists. Patients are prioritized according to case severity and clinical need. However, it is not known whether compensation status is associated with waiting times. METHODS: Patients with sciatica from herniated lumbar disc, confirmed on advanced imaging, were registered for surgery. Information was collected on 393 patients, 66 (17%) who were receiving workers' compensation or personal disability insurance. Waiting time was calculated from registration to surgery and back pain and leg pain intensities were assessed by surgeons, using an 11-point numerical rating scale. Weekly probabilities of remaining on the waitlist were estimated using Kaplan-Meier methods. Patients undergoing emergency surgery or waiting longer than 12 months were censored. Waiting times were compared using the log-rank test, and Cox regression was used to estimate the effect of compensation on waiting after controlling for confounders. RESULTS: Pain intensity, neurologic status, and symptom duration were associated with waiting time. Compensation status was associated with a lower, statistically nonsignificant, likelihood of undergoing ESLD; hazard ratio (HR) = 0.83 (95% CI: 0.63-1.11) and the effect was attenuated with adjustment; HR = 1.02 (95% CI: 0.76-1.38). The median adjusted waiting time for surgery was 7 weeks among noncompensated and compensated patients. CONCLUSION: These results provide evidence that, contrary to conventional wisdom, compensation status was not associated with longer waits for ESLD. While patients receiving compensation have elsewhere been observed to have worse outcomes after discectomy, our results suggest this is unlikely to be due to delays imposed by queuing.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".