Predictive value of 3-month lumbar discectomy outcomes in the NeuroPoint-SD Registry
Bibliographic record
Abstract
OBJECT: The authors have established a multicenter registry to assess the efficacy and costs of common lumbar spinal procedures using prospectively collected outcomes. Collection of these data requires an extensive commitment of resources from each site. The aim of this study was to determine whether outcomes data from shorter-interval follow-up could be used to accurately estimate long-term outcome following lumbar discectomy. METHODS: An observational prospective cohort study was completed at 13 academic and community sites. Patients undergoing single-level lumbar discectomy for treatment of disc herniation were included. SF-36 and Oswestry Disability Index (ODI) data were obtained preoperatively and at 1, 3, 6, and 12 months postoperatively. Quality-adjusted life year (QALY) data were calculated using SF-6D utility scores. Correlations among outcomes at each follow-up time point were tested using the Spearman rank correlation test. RESULTS: One hundred forty-eight patients were enrolled over 1 year. Their mean age was 46 years (49% female). Eleven patients (7.4%) required a reoperation by 1 year postoperatively. The overall 1-year follow-up rate was 80.4%. Lumbar discectomy was associated with significant improvements in ODI and SF-36 scores (p < 0.0001) and with a gain of 0.246 QALYs over the 1-year study period. The greatest gain occurred between baseline and 3-month follow-up and was significantly greater than improvements obtained between 3 and 6 months or 6 months and 1 year(p < 0.001). Correlations between 3-month, 6-month, and 1-year outcomes were similar, suggesting that 3-month data may be used to accurately estimate 1-year outcomes for patients who do not require a reoperation. Patients who underwent reoperation had worse outcomes scores and nonsignificant correlations at all time points. CONCLUSIONS: This national spine registry demonstrated successful collection of high-quality outcomes data for spinal procedures in actual practice. Three-month outcome data may be used to accurately estimate outcome at future time points and may lower costs associated with registry data collection. This registry effort provides a practical foundation for the acquisition of outcome data following lumbar discectomy.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".