Patients Undergoing Revision Microdiskectomy for Recurrent Lumbar Disk Herniation Experience Worse Clinical Outcomes and More Revision Surgeries Compared With Patients Undergoing a Primary Microdiskectomy
Bibliographic record
Abstract
INTRODUCTION: Recurrent disk herniation treatment aims to optimize outcomes. This study compares the demographics and patient-reported outcomes of patients who underwent primary or revision lumbar microdiskectomy surgery for recurrent disk herniation. METHODS: A retrospective cohort analysis was performed of consecutive patients who underwent primary or revision lumbar microdiskectomies between January 2008 and December 2015. Patients were divided into two groups: primary (primary) and revision (recurrent). Herniated disks were confirmed preoperatively using MRI. Patient-reported outcomes included Visual Analog Scales (VAS) scores for the back and leg, Oswestry Disability Index scores, 12-Item Short Form Mental and Physical Survey scores, and the Veterans RAND 12-Item Health Mental and Physical Survey scores. RESULTS: One hundred ten patients met inclusion criteria: 72 from primary cohort and 38 from recurrent cohort. Recurrent patients experienced presurgical symptoms for significantly less time. On bivariate analysis, recurrent patients reported significantly worse preoperative VAS-back and VAS-leg scores. On multivariate analysis, recurrent patients reported significantly worse postoperative VAS-back, VAS-leg, and Oswestry Disability Index scores. Recurrent patients were less likely to be satisfied with surgical outcomes and to feel that surgery had met or exceeded their expectations. CONCLUSION: Patients undergoing revision microdiskectomy are likely to experience worse postoperative symptoms and disability relative to patients undergoing primary microdiskectomy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".