Minimally Invasive Spinal Surgery in the Elderly
Bibliographic record
Abstract
Lumbar degenerative disease can have varied pathoanatomy, with stenosis, spondylolisthesis, and scoliosis contributing to significant pain and disability. Among appropriately selected patients, surgical intervention can treat both back pain and leg pain and improve quality of life in a cost-effective manner with an acceptable safety profile. The evolution of minimally invasive surgical (MIS) techniques offers the potential to decrease the physiological impact of surgery and to improve the complication profile while achieving the same spine surgical objectives. The utility of such techniques among elderly patients >65 years of age has not been rigorously evaluated, and this systematic review sought to define the utility and safety of MIS spinal surgery for decompression, interbody fusion, and deformity correction in this population. Review of 2 studies for MIS lumbar decompression reveals that the majority of elderly patients exhibit significant improvements in pain (change in visual analog score for leg pain, 3.4 points) and disability (change in Oswestry Disability Index, 19 points), with inadvertent durotomy in 3% of patients. Review of 4 studies for MIS lumbar interbody fusion reveals robust improvement in pain (change in visual analog score for leg pain, 3.4 points; change in visual analog score for back pain, 7.2 points), with inadvertent durotomy in 5% of patients. Narrative review was performed for adult degenerative deformity correction, revealing that MIS techniques are feasible for managing such patients with acceptable rates of osseous union and complication. On the basis of largely low-quality, retrospective evidence, we recommend that elderly patients should not be excluded from MIS interventions for symptomatic lumbar degenerative spinal disease.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".