Lumbar Microdiscectomy: A Clinicoradiological Analysis of Outcome
Bibliographic record
Abstract
BACKGROUND: The long-term outcome after lumbar microdiscectomy (LMD) may be affected by low back pain (LBP) and segmental instability, the determinants of which remain unclear. We sought to analyze the interaction between clinical, functional, and radiological variables and their impact on patient outcome. METHODS: All patients who underwent LMD in 2004-2005 were invited to participate in this retrospective cohort study. Patients were re-evaluated clinically and radiologically after a three to five year follow-up. RESULTS: Forty-one of 97 eligible patients were enrolled. Twelve patients (29.3%) reported moderate-to-severe sciatica, 12 (29.3%) had moderate LBP, and 13 (31.7%) exhibited clinical evidence of segmental instability. Thirty-eight patients (92.7%) had minimal disability and 3 (7.3%) had moderate disability. Twenty-three patients (56.1%) were fully satisfied, while 18 (43.9%) had only partial satisfaction, having expected a better outcome. Thirty-three patients (80.5%) returned to full-time work. Median disc space collapse (DSC) was 20% (range 5-66%) and L4-L5 was particularly affected. Prevalence of Modic changes increased from 46.3% to 78% with type 2 predominance. Multivariate logistic regression analysis identified the following negative prognostic factors: female sex, young age, lack of regular exercise, and chronic preoperative LBP. There was no correlation between the course of Modic changes, DSC, and patient outcome. CONCLUSION: Although many patients may be symptomatic following LMD, significant disability and dissatisfaction are uncommon. Female sex, young age, lack of exercise, and chronic preoperative LBP may predict a worse outcome. Disc collapse is a universal finding, particularly at L4-L5. Neither DSC nor Modic changes seem to affect patient outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".