When Would Minimally Invasive Spinal Surgery Not Be Preferable for Metastatic Spine Disease?
Bibliographic record
Abstract
BACKGROUND: Metastatic spine tumor surgery (MSTS) is an important treatment modality of metastatic spinal disease (MSD). Open spine surgery (OSS) was previously the gold standard of treatment till the early 2010s. However, advancements in MSTS in recent years have led to the advent of minimally invasive spinal surgery (MISS) techniques for the treatment of MSD. The clear benefits of MISS have resulted in a current paradigm shift toward today's gold standard of MISS and early adjuvant radiotherapy in treating MSD patients. Nonetheless, despite improvements in surgical techniques and the rise of literature supporting MISS for MSD, there are still certain situations whereby MISS is not desirable or even suitable. There has also yet to be any literature describing the considerations of not using MISS in MSD in today's clinical context. METHODS: A narrative review was conducted for this manuscript. All studies related to OSS and MISS in MSTS were included. RESULTS: A total of 54 studies were included in this review. These studies discussed various advantages of MISS for MSD in today's clinical context, including the patient profile, location of vertebrae involved with metastasis requiring treatment, tumor characteristics, as well as equipment availability. CONCLUSION: This study establishes situations in which MISS can be less applicable despite the advantages it may confer over traditional OSS. MSTS should be individualized, depending on the experience of the surgeon. OSS is a time-tested approach that still holds weight in MSTS and should be readily utilized depending on the clinical situation.
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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 | 0.004 | 0.025 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".