Overview of Molecular Prognostication for Common Solid Tumor Histologies – What the Surgeon Should Know
Bibliographic record
Abstract
STUDY DESIGN: Narrative Literature review. OBJECTIVE: To provide a general overview of important molecular markers and targeted therapies for the most common neoplasms (lung, breast, prostate and melanoma) that metastasize to the spine and offer guidance on how to best incorporate them in the clinical setting. METHODS: A narrative review of the literature was performed using PubMed, Google Scholar, Medline databases, as well as the histology-specific National Comprehensive Cancer Network guidelines to identify relevant articles limited to the English language. Relevant articles were reviewed for commonly described molecular mutations or targeted therapeutics, as well as associated clinical outcomes, and surgery-related risks. RESULTS: Molecular markers and targeted therapies have dramatically improved the survival of cancer patients. The increasing importance of prognostic molecular markers and targeted therapies provides rationale for their incorporation into clinical decision-making for patients diagnosed with metastatic spine disease. In this review, we discuss the molecular markers/mutations and targeted therapies associated with the most common malignancies that metastasize to the spine and provide a framework that the surgeon can utilize when evaluating patients for potential intervention. Finally, we provide case examples that highlight the importance of molecular prognostication and therapies in surgical decision-making. CONCLUSION: An integrated understanding of the implications of surgery, radiation, molecular markers and targeted therapies that guide prognostication and treatment is warranted in order to achieve the most favorable outcomes for patients with metastatic spine disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".