MétaCan
Menu
← Back to cohort
Record W1576851948 · doi:10.1161/str.46.suppl_1.106

Abstract 106: Validation Of The Unruptured Intracranial Aneurysm Treatment Score (UIATS) to Guide Management of Unruptured Intracranial Aneurysms

2015· article· en· W1576851948 on OpenAlexaff
Nima Etminan, Robert D. Brown, Kerim Beseoglu, Seppo Juvela, Akio Morita, James C. Torner, Jean Raymond, Colin P. Derdeyn, Andreas Raabe, J Mocco, Amr Abdulazim, Miikka Korja, E. Sander Connolly, Helmuth Steinmetz, Giuseppe Lanzino, A. Pasqualin, Daniel A. Rüfenacht, Peter D. LeRoux, Peter Vajkoczy, Cameron M. McDougall, Daniel Hänggi, Gabriël J.E. Rinkel, R. Loch Macdonald

Bibliographic record

VenueStroke · 2015
Typearticle
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsUniversity of TorontoHôpital Notre-Dame
Fundersnot available
KeywordsMedicineMultidisciplinary approachLikert scaleAneurysmNeuroradiologyNeurosurgeryNeurovascular bundleNeurologyTest (biology)Delphi methodMedical physicsPhysical therapyRadiologySurgeryPsychiatryArtificial intelligenceStatistics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.036
metaresearch head score (Gemma)0.090
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.191

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.090
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.033
GPT teacher head0.287
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueStroke→Same topicIntracranial Aneurysms: Treatment and Complications→French-language works237,207→