P.129 Patterns of spinal metastatic disease and mechanical instability: a retrospective correlation with tumor histology
Bibliographic record
Abstract
Background: This study aims to provide epidemiological data concerning spinal instability and patterns of metastatic invasion of the spine based on tumor histology. Methods: We allocated 285 patients with spinal metastatic disease through a retrospective review. SINS was calculated using good-quality computed tomography (CT) imaging studies. Spinal metastases were also grouped into intracompartmental, extracompartmental or multiple metastases. Results: Esophageal cancer was the least likely to be associated with instability with about 64% of cases being stable. The highest rate of instability scores was observed in breast carcinoma with 18% of cases graded as unstable. Renal cell carcinoma was associated with lytic spinal metastases whereas blastic metastases mostly occurred in prostate carcinoma (P<0.001). Whereas 68.1% of cases represented multiple metastases, the remainder was associated with either intracompartmental (13.3%) or extracompartmental (18.6%) disease. The highest degrees of spinal instability (intermediate and unstable categories) were associated with extra-compartmental metastatic disease (P<0.001). Conclusions: This study sheds light on the patterns of spinal metastatic disease and mechanical instability on the basis of tumor histology, utilizing standardized scoring systems. The utilization of such scoring systems allows for a standardized approach towards description and analysis of spinal metastasis facilitating clinical research in this avenue.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".