Abstract 123: The Unruptured Intracranial Aneurysm Treatment Score (UAITS) - Proposal of a Multidisciplinary Research Group
Bibliographic record
Abstract
Objective: Unruptured intracranial aneurysms (UIAs) are being identified with increasing frequency. Their natural history is being defined but their appropriate treatment remains controversial. We convened a panel of experts with the aim being the development of a treatment score (UIATS) that could guide decision making for treatment of UIAs. Method: An international, multidisciplinary (Neurology, Clinical Epidemiology, Neurosurgery and Neuroradiology) panel of experts on research and treatment of cerebral aneurysms was formed. Panel members were chosen to be geographically and professionally dispersed, to increase the validity of the score. A 5-round, Delphi consensus process was initiated to identify and rate all features, relevant to assess UIAs and their treatment based on current evidence and practice. Rating scales and risk percentages were repeatedly used to determine statistical weight for each factor and to exclude significant discrepancies between rounds. Medians from all rounds were then transformed into corresponding scores for every item to create the scoring system. Representative cases of patients with UIAs were used to test decision-making and the level of acceptance as well to validate the score. Results: A minimum of 85% of 39 experts, from 12 different countries participated in the first 4 rounds. More than 60 relevant features were initially listed by the panel. These items were subsequently rated repeatedly, until the least relevant items were omitted. The UIATS system was then created based on 13 highly relevant items. Despite some heterogeneity for individual ratings, there were no significant inconsistencies throughout the rounds. The system comprises different features of 3 main categories (patient-, aneurysm - and treatment-related factors), which all add up to one specific ratio. This ratio reflects the sum of factors supporting treatment or conservative management for UIAs, based on individual medical constellations. Conclusion: Following internal and external validation, this scoring system, derived from a consensus among a large, international and multidisciplinary group of experts, may aid clinicians to decide about the expected natural history and treatment risk of an UIA.
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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 | 0.060 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".