Contemporary trends in the incidence and timing of spinal metastases: A population-based study
Bibliographic record
Abstract
Background: Spinal metastases are a significant complication of advanced cancer. In this study, we assess temporal trends in the incidence and timing of spinal metastases and examine underlying patient demographics and primary cancer associations. Methods: = 37, 375 patients identified with spine metastases). Primary outcomes were annual incidence of spinal metastasis, and time to metastasis after primary diagnosis. Results: The age-standardized incidence of spinal metastases increased from 229 to 302 cases per million over the 13-year study period. The average annual percent change (AAPC) in incidence was 2.2% (95% CI: 1.4% to 3.0%) with patients aged ≥85 years demonstrating the largest increase (AAPC 5.2%; 95% CI: 2.3% to 8.3%). Lung cancer had the greatest annual incidence, while prostate cancer had the greatest increase in annual incidence (AAPC 6.5; 95% CI: 4.1% to 9.0%). Lung cancer patients were found to have the highest risk of spine metastasis with 10.3% (95% CI: 10.1% to 10.5%) of patients being diagnosed at 10 years. Gastrointestinal cancer patients were found to have the lowest risk of spine metastasis with 1.0% (95% CI: 0.9% to 1.0%) of patients being diagnosed at 10 years. Conclusions: The incidence of spinal metastases has increased in recent years, particularly among older patients. The incidence and timing vary substantially among different primary cancer types. These findings contribute to the understanding of disease trends and emphasize a growing population of patients who require subspecialty care.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".