Biologically Effective Dose and Dose Rate in Gamma Knife Radiosurgery for Trigeminal Neuralgia: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Purpose Gamma Knife radiosurgery (GKRS) is used in trigeminal neuralgia (TN) to deliver precise, focused ionizing radiation to the trigeminal nerve to reduce its ability to transmit pain signals. Understanding the impact of unique radiobiological parameters, such as biological effective dose (BED) and dose-rate in GKRS is essential to optimize treatment protocols and ensure predictable therapeutic outcomes. We conducted a systematic review and meta-analysis to evaluate how BED and dose-rate impact GKRS effectiveness and toxicity. Methods We searched medical and healthcare databases from inception to May 19, 2024, for publications that report on the impact of GKRS BED and/or dose-rate on TN outcomes. Following two rounds of screening conducted in duplicate, we used a random-effects meta-analysis and meta-regression to examine the association between dose-rate and pain relief. Results Of 6,950 citations identified, eight publications reported data on BED and dose-rate association with GKRS patient outcomes in TN. Eight cohorts reported on 2,596 patients in six countries. The "beam-on" time ranged from 27 to 171 minutes, with a prescription dose ranging from 62.5 to 95 Gy. The median BED 2.47 was 2,105 Gy (range: 1968-2675), median dose-rate was 2.2 Gy/min (2.06-2.81), and median maximum brainstem dose was 20.7 Gy (14.8-34.7). Meta-analysis suggested higher dose-rates may be associated with higher rates of pain relief (relative risk [95% CI] = 1.36 [1.10-1.67], p = 0.005). Meta-regression demonstrated a non-significant relationship between dose-rate and pain relief with an estimated 26% increase in chance of pain control for each 1 Gy/min increase in median dose-rate (β [95% CI] = 0.26 [-1.09, 1.60], p = 0.71). Conclusion Higher dose-rates in GKRS may be associated with better pain relief in TN. Dose-rate should be considered in the treatment of TN when using GKRS.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.040 |
| Bibliometrics | 0.005 | 0.007 |
| 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".