Assessing the real benefits of surgery for degenerative lumbar spinal stenosis without instability and spondylolisthesis: a single surgeon experience with a mean 8-year follow-up
Bibliographic record
Abstract
BACKGROUND: The degenerative lumbar spinal stenosis is one of the most commonly treated spinal disorders in older adults; despite its increasing frequency, it is not yet clear what the most effective therapy might be. The aim of this study is to investigate the very long term results of a homogenized cohort of patients suffering from lumbar spinal stenosis: the first subset of patients operated on with laminectomy and the second subset of patients was also advised to undergo laminectomy but never operated on. METHODS: Patients from both subgroups were advised to undergo surgery, according to the same criteria, in the period between 2000 and 2010 and were re-evaluated in the period between January and December 2016. RESULTS: Comparing the two subsets of patients, both suffering from clinically relevant LSS, the first subset returns a statistically significant clinical improvement at follow-up. The rate of excellent results decreases over years. Iatrogenic spinal instability incidence was found to be 3.8% in the present cohort. CONCLUSIONS: Although the improvement of the first postoperative years decreases over time and despite the lack of general consensus, the lack of established shared guidelines and the limitations of this research, the results support the utilisation of surgery for the management of this condition. LEVEL OF EVIDENCE: 3.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".