Abstract 106: Validation Of The Unruptured Intracranial Aneurysm Treatment Score (UIATS) to Guide Management of Unruptured Intracranial Aneurysms
Bibliographic record
Abstract
Objective: We previously derived the Unruptured Intracranial Aneurysm Treatment Score (UIATS), designed using a multidisciplinary consensus approach among neurovascular specialists from diverse geographic and practice backgrounds. Here, we report on the development and validation of the final version of UIATS. Method: An international, multidisciplinary (neurosurgery, neuroradiology, neurology, clinical epidemiology) group of 69 (39 panel members and 30 blinded external reviewers) specialists in the research and treatment of UIAs was convened. A web survey-based Delphi consensus process consisting of 7 rounds was utilized to rate numerous features of potential relevance in the assessment and treatment of UIAs and to develop the UIATS. Mean ratings were repeatedly used to determine statistical weight for each factor and then transformed into corresponding scores for every item to create the UIATS. For internal and blinded external validation, 30 representative cases of patients with UIAs were used to test the level of agreement (5 point Likert Scale) with treatment recommendations based on the UIATS. Results: The final UIATS system was designed in three domains (patient-, aneurysm - and treatment-related), comprising 13 different categories and 29 different features (Figure 1). Mean agreement based on Likert scores (5 indicating strong agreement and 1 indicating strong disagreement) was 4·2 for both reviewer cohorts, whereas mean agreement per case was 4·2 (panel members) and 4·5 (external reviewers) (p=0·017, Mann-Whitney-U Test). Conclusion: The final version of UIATS system was internally and externally validated by a large multidisciplinary group of neurovascular specialists, which suggests that the UIATS reflects contemporary decision-making regarding management of a patient with an UIA. The UIATS may aid clinicians in deciding on the appropriate management for 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 | 0.036 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".