Responses to ARUBA: a systematic review and critical analysis for the design of future arteriovenous malformation trials
Bibliographic record
Abstract
OBJECTIVE The ARUBA study (A Randomized Trial of Unruptured Brain Arteriovenous Malformations [AVMs]) on unruptured brain AVMs has been the object of comments and editorials. In the present study the authors aim to systematically review critiques, discuss design issues, and propose a framework for future trials. METHODS The authors performed a systematic review of the French and English literature on the ARUBA study published between January 2006 and February 2015. The electronic search, including the Cochrane Library, MEDLINE (PubMed and Ovid), CINAHL, and EMBASE databases, was complemented by hand searching and cross-referencing. The comments were categorized as items related to the design, the conduct, and the analysis and interpretation of the trial. RESULTS Thirty-one articles or letters were identified. The pragmatic design, with heterogeneity of patients and lack of standardization of the treatment arm, were frequently stated concerns. The choice of outcome measures was repeatedly criticized. During the trial, low enrollment rates, selection bias, and premature interruption of enrollment were frequent comments. The short follow-up period, the lack of subgroup analyses, the lack of details on the results of the various treatments, and a contentious interpretation of results were noted at the analysis stage. A fundamental problem was the primary hypothesis testing conservative management. The authors believe that other trials are needed. Future trials could be pragmatic, test interventions stratified at the time of randomization, and look for long-term, hard clinical outcomes in a large number of patients. CONCLUSIONS In the authors' view, the ARUBA trial is a turning point in the history of brain AVM management; future trials should aim at integrating trial methodology and clinical care in the presence of uncertainty.
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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 | 0.575 | 0.820 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.021 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".