Contemporary Treatment Strategy for Spinal Metastasis: The “LMNOP” System
Bibliographic record
Abstract
The choice of treatment for spinal metastasis is complex because (1) it depends on several inter-related clinical and radiologic factors, and (2) a wide range of management options has evolved in recent years. While radiation therapy and surgery remain the cornerstones of treatment, radiosurgery and percutaneous vertebral augmentation have also established a role. Classification systems have been developed to aid in the decision-making process, and each has different strengths and weaknesses. The comprehensive scoring systems developed to date provide an estimate of life expectancy, but do not provide much advice on the choice of treatment. We propose a new decision model that describes the key factors in formulating the management plan, while recognizing that the care of each patient remains highly individualized. The system also incorporates the latest changes in technology. The LMNOP system evaluates the number of spinal Levels involved and the Location of disease in the spine (L), Mechanical instability (M), Neurology (N), Oncology (O), Patient fitness, Prognosis and response to Prior therapy (P).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".