Cost consequence analysis of waiting for lumbar disc herniation surgery
Bibliographic record
Abstract
The economic repercussions of waiting for lumbar disc surgery have not been well studied. The primary goal of this study was to perform a cost-consequence analysis of patients receiving early vs late surgery for symptomatic disc herniation from a societal perspective. Secondarily, we compared patient factors and patient-reported outcomes. This is a retrospective analysis of prospectively collected data from the CSORN registry. A cost-consequence analysis was performed where direct and indirect costs were compared, and different outcomes were listed separately. Comparisons were made on an observational cohort of patients receiving surgery less than 60 days after consent (short wait) or 60 days or more after consent (long wait). This study included 493 patients with surgery between January 2015 and October 2021 with 272 patients (55.2%) in the short wait group and 221 patients (44.8%) classified as long wait. There was no difference in proportions of patients who returned to work at 3 and 12-months. Time from surgery to return to work was similar between both groups (34.0 vs 34.9 days, p = 0.804). Time from consent to return to work was longer in the longer wait group corresponding to an additional $11,753.10 mean indirect cost per patient. The short wait group showed increased healthcare usage at 3 months with more emergency department visits (52.6% vs 25.0%, p < 0.032), more physiotherapy (84.6% vs 72.0%, p < 0.001) and more MRI (65.2% vs 41.4%, p < 0.043). This corresponded to an additional direct cost of $518.21 per patient. Secondarily, the short wait group had higher baseline NRS leg, ODI, and lower EQ5D and PCS. The long wait group had more patients with symptoms over 2 years duration (57.6% vs 34.1%, p < 0.001). A higher proportion of patients reached MCID in terms of NRS leg pain at 3-month follow up in the short wait group (84.0% vs 75.9%, p < 0.040). This cost-consequence analysis of an observational cohort showed decreased costs associated with early surgery of $11,234.89 per patient when compared to late surgery for lumbar disc herniation. The early surgery group had more severe symptoms with higher healthcare utilization. This is counterbalanced by the additional productivity loss in the long wait group, which likely have a more chronic disease. From a societal economic perspective, early surgery seems beneficial and should be promoted.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".