P.230 Modic changes and clinical outcomes in patients undergoing lumbar surgery for disc herniation
Bibliographic record
Abstract
Background: Lumbar disc herniation (LDH) is a risk factor for Modic change (MC) development on spinal MRI. MC has been associated with worse pre- and post-operative pain, disability, and health-related quality of life (HRQoL). We examined the relationship between pre-operative MC and post-operative assessment scores for patients receiving discectomy (LD) or transforaminal interbody fusion (TLIF) for LDH. Methods: We reviewed 285 primary single-level surgeries. Pre-operative and 12-month post-operative assessment scores: Visual Analog Scale Leg-Pain (VAS-LP), Oswestry Disability Index (ODI), and Short-Form-36 Physical Component Summary (SF-36-PCS). MC subgroup on pre-operative MRI was recorded by a single neuroradiologist. Results: 179 patients were included. The sample prevalence of MC on pre-operative MRI was 62%; MC2 was most common (35%). No differences in pre-operative scores were identified, regardless of present or absent MC. For the overall cohort, improvement in assessment scores were observed: SF-36 improved an average of 8.2 points (95% CI: [5.8, 10.7]), ODI by 11.3 points (95% CI: [8.7, 14.0]), and VAS by 2.8 points (95% CI: [2.1, 3.5]). In nearly all cases, MCID values were met. Conclusions: Clinically significant improvement in post-operative pain, disability, and HRQoL was observed for both procedures. Modic change on pre-operative MRI was not associated with worse clinical assessment scores.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".