OUTCOME PREDICTION FOLLOWING LUMBAR DISC SURGERY (OPTIDISC): A LONGITUDINAL STUDY OF OUTCOME TRAJECTORIES, PROGNOSTIC FACTORS, AND RISK MODELS
Bibliographic record
Abstract
Although lumbar discectomy for radiculopathy effectively reduces pain and disability for most patients, some report continued pain following surgery. Information to predict patient outcomes following discectomy could assist surgeons with patient selection. This study aimed to i) describe the perioperative trajectories of leg pain and overall clinical outcome following lumbar disc surgery for radiculopathy, ii) identify the preoperative prognostic factors that predict trajectories representing poor clinical outcomes, and iii) develop and internally validate multivariable prognostic models. This cohort study included patients enrolled in the Canadian Spine Outcomes and Research Network diagnosed with lumbar disc pathology and radiculopathy who underwent lumbar discectomy at one of 18 spine centers. Potential outcome predictors included preoperative demographic, health-related, and clinical prognostic factors. Clinical outcomes were univariable latent-class trajectories of leg pain intensity (numeric pain rating scale) and overall outcomes modelled with multivariable trajectories of leg and back pain intensity and pain-related disability (Oswestry index). Multivariable risk model performance and internal validity were evaluated with discrimination and calibration statistics based on bootstrap shrinkage with 500 resamplings. We included data from 1,142 patients (47.6% female) operated on by one of 66 surgeons. The trajectory models identified 3 subgroups, with 11.4% of patients in the leg pain model and 28.2% in the overall outcome model experiencing a poor clinical outcome. Eleven demographic, health, and clinical factors predicted patients’ leg pain and overall outcomes. The performance of the leg pain risk model was inadequate. The overall outcome model had acceptable discrimination, calibration, and evidence of internal validity in predicting patients at risk of experiencing a poor outcome following discectomy. Patients experienced heterogeneous outcomes following lumbar discectomy that were associated with numerous preoperative prognostic factors. A multivariable risk model adequately predicted the overall outcomes experienced by patients. This tool can assist with patient selection for lumbar discectomy but requires additional replication and validation before confident clinical implementation.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".