Postoperative recovery patterns following discectomy surgery in patients with lumbar radiculopathy
Bibliographic record
Abstract
This retrospective study of prospectively collected data aimed to identify unique pain and disability trajectories in patients following lumbar discectomy surgery. Patients of this study population presented chiefly with lumbar radiculopathy and underwent discectomy surgery from thirteen sites enrolled in the CSORN registry. Outcome variables of interest included numeric rating scales for leg/back pain and modified Oswestry disability index scores at baseline, 3, 12, and 24 months post-operatively. Latent class growth analysis was used to identify distinct courses in each outcome. Data from 524 patients revealed three unique trajectories for leg pain (excellent = 18.4%, good = 55.4%, poor = 26.3%), disability (excellent = 59.7%, fair = 35.6%, poor = 4.7%) and back pain (excellent = 13.0%, good = 56.4%, poor = 30.6%). Construct validity was supported by statistically significant differences in the proportions of patients attaining the criteria for minimal important change (MIC; 30%) or clinical success in disability (50% or Oswestry score ≤ 22) (p < 0.001). The variable proportions of patients belonging to poor outcome trajectories shows a disconnect between improved disability and persistence of pain. It will be beneficial to incorporate this information into the realm of patient expectation setting in concert with future findings of potential factors predictive of subgroup membership.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".