Letter: Radiosurgery With Prior Embolization Versus Radiosurgery Alone for Intracranial Arteriovenous Malformations: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
To the Editor: I read with great interest an article published in the latest issue of Neurosurgery. The authors evaluated the efficacy and safety of radiosurgery combined with adjunctive embolization compared with radiosurgery alone in the treatment of intracranial arteriovenous malformations by incorporating studies available in the literature. The authors used strict and comprehensive inclusion and exclusion criteria in the conception and design of the full article.1 However, I would like to point out the following shortcomings to the authors and readers for consideration in future studies. A significant problem stems from the potential flaws in selecting tools to evaluate research quality. In the methods section, the authors underscore their reliance on the Newcastle-Ottawa Scale to determine the quality of case-control and cohort studies. However, this scoring system is impeded by clear limitations. The inflexibility and subjectivity of its scoring criteria may lead to discrepancies in the assessments conducted by different reviewers. Moreover, some scholars believe that the Newcastle-Ottawa Scale falls short in addressing publication bias and in conducting a comprehensive evaluation of controls for confounding factors, which are crucial for a thorough assessment of research quality.2 Therefore, to enhance the evaluation of nonrandomized studies, I advocate for the use of the Methodological Index for Non-Randomized Studies3 or the Downs and Black tool4 because they offer a more comprehensive assessment of research quality. In addition, I need to highlight several shortcomings in the text. First, in the forest plots presented within the document, we noticed that some included trials have a small sample size.5-7 However, there is a lack of assessment for the power calculation for each study. Therefore, I suggest that the authors conduct a sensitivity analysis to test whether the results remain robust after excluding studies with small sample sizes. Second, the authors only searched 3 databases, which could lead to an incomplete retrieval of relevant literature. Third, the document lacks an evaluation of the grading of outcome measures. Finally, considering that the included studies are primarily retrospective, the conclusions drawn still require validation by high-quality randomized controlled trials.
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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 | 0.016 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".