Is Spinal Stenosis Better Treated Surgically or Nonsurgically?
Bibliographic record
Abstract
For patients with clinical and radiographic lumbar spinal stenosis, is surgery or continued nonsurgical treatment a better option for improvements in baseline disability scores; and what proportion of patients treated surgically and nonsurgically get better, worse, or remain the same with time? We prospectively evaluated 125 consecutive patients for this non-randomized cohort study. Of the patients choosing surgery, 54 underwent decompression only and 42 had decompression and fusion for preexisting spondylolisthesis; twenty-nine patients declined surgery. At 2 years followup, the average improvements in Roland-Morris questionnaire score in the decompression only, decompression with fusion, and nonsurgical groups were 6.9, 6.1, and 1.2, respectively. The percentages of patients who were better, worse, or the same were similar for those who had decompression only (63.3%, 4.1%, and 32.7%, respectively) and decompression with fusion (61.5%, 2.6%, and 35.9%, respectively) but different from those treated without surgery (25.0%, 12.5%, and 62.5%, respectively). We observed no occurrences of cauda equina syndrome or severe neurologic dysfunction in any of the groups after 2 years. A majority of patients declining surgery had persistent symptoms. The majority of patients who choose surgery will be improved but will have residual symptoms and therefore should be counseled about realistic expectations.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.001 | 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".