Evaluating the Outcomes of Vertebral Biopsies Performed in Osteoporotic Vertebral Fractures: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background/Objectives: Osteoporotic vertebral fractures (OVFs) are common in older adults. While surgery is generally reserved for unstable or painful fractures, some OVFs conceal underlying malignancies, including metastatic and hematologic cancers. This study aimed to determine the pooled prevalence of unsuspected malignancy in patients initially diagnosed with OVFs. Methods: A systematic search of PubMed and Scopus was conducted from inception to September 2025 in accordance with PRISMA guidelines. Eligible studies included adults with presumed OVFs who underwent vertebral biopsy and histopathological evaluation. Prevalence estimates were pooled using a random-effects model, and study quality was assessed with the Newcastle–Ottawa Scale. Results: Thirteen studies involving 3513 patients were included. The pooled prevalence of malignancy was 8.0% (95% CI: 5.4–10.6), comprising metastatic solid tumors (4.9%; 95% CI: 2.3–7.4) and multiple myeloma (2.6%; 95% CI: 1.3–3.9). Malignancy was detected in 2.7% (95% CI: 1.8–4.1) of routine biopsy cohorts versus 36.8% (95% CI: 22.1–54.4) of clinically suspected cases. Diagnostic yield exceeded 45% in patients selected by combined history, imaging, or known malignancy. No biopsy-related complications or procedure-related mortality were reported. Moderate heterogeneity was observed, mainly in suspected cohorts. Conclusions: Vertebral biopsy is a safe and diagnostically valuable procedure in vertebral compression fractures. Its yield ranges from about one in 30 patients in routine settings to nearly one in two in high-risk groups, underscoring the importance of structured patient selection to facilitate timely cancer detection and referral.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".