Frame-based versus frameless stereotactic brain biopsies: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Stereotactic brain biopsy techniques have been a focus of rapid technological innovation. The recent advent of frameless stereotaxy has invited the question of whether it can provide the same diagnostic yield as frame-based techniques, without increasing risk of harm to patients. The goal of this meta-analysis was to compare each of these techniques in terms of yield and safety. Methods: We independently searched four databases for English studies comparing frameless and frame-based stereotactic brain biopsies. Our primary outcome was biopsy diagnostic yield. Our secondary outcomes included mortality, morbidity (e.g., symptomatic postbiopsy intracranial hemorrhage, asymptomatic postbiopsy intracranial hemorrhage, new postbiopsy neurological deficit, and postbiopsy seizure), and frequency of repeat biopsy. We calculated pooled estimates and relative risks for dichotomous outcomes using Review Manager 5.3, with corresponding 95% confidence intervals. Results: A total of 3256 stereotactic brain biopsies (2050 frame based and 1206 frameless), from 20 studies, were included in our final analysis. The results did not demonstrate any significant difference between the two stereotactic systems in terms of diagnostic yield (risk ratio [RR] 1.00, 95% confidence interval [CI] 0.99–1.02, P = 0.64, I2 = 0%). The only significant difference was the increased frequency of asymptomatic hemorrhages in the frameless group (RR 1.37, 95% CI 1.06–1.75, P = 0.01, I2 = 0%). Application of Grading of Recommendations Assessment, Development, and Evaluation to the results yielded very low quality of all outcomes. Conclusion: Based on very low-quality evidence, both frame-based and frameless stereotaxy are safe and effective for biopsy of intracranial tumors. Further study of patient preference and cost comparing analysis is required to identify if either modality should be preferred.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".