The radiation dose tolerance of the brachial plexus: A systematic review and meta-analysis
Bibliographic record
Abstract
Purpose We performed a systematic review and meta-analysis of studies reporting the incidence of radiation induced brachial plexopathy (RIBP) and the associated radiotherapy doses to this structure. Methods Databases were queried without language restriction for cohort studies reporting RIBP incidence and associated brachial plexus dose maximum dose (bpDmax). Studies specifying RIBP relative risk (RR) effect size were selected for meta-analysis. RRs for RIBP from each study were converted to a regression coefficient (β) and standard error corresponding to a continuous representation of bpDmax. The adjusted β from individual studies were combined using a random effects model and weighted by inverse variance (1/SE 2 ). The trim and fill approach was used to assess publication bias. Results We identified 25 studies that included 37 unique patient cohorts eligible for analysis. Seventeen cohorts experienced an RIBP incidence ≤5%, of which 6 cohorts exceeded conventional plexus constraints of 60 Gy for bpDmax. Five of the 6 cohorts were simulated with 3D-CT techniques. Meta-analysis of eligible studies demonstrated a significant increase in RIBP risk for each Gy increase in bpDmax (RR, 1.11; 95% CI 1.07–1.15). Results remained significant after adjustment for publication bias and when sensitivity analysis was performed. Conclusions Our results suggest that current brachial plexus constraints of 60–66 Gy are safe. Meta-analysis provides a log-linear model to quantify the association of brachial plexus dose and RIBP risk, and thus inform the therapeutic ratio for dose escalation. Further prospective studies reporting dosimetric data can better refine this model and inform brachial plexus constraint guidelines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.064 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.053 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".