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Record W4400787366 · doi:10.1007/s00062-024-01435-x

Comparative Efficacy of Flow Diverter Devices in the Treatment of Carotid Sidewall Intracranial Aneurysms: a Retrospective, Multicenter Study

2024· article· en· W4400787366 on OpenAlexaff
Adam A. Dmytriw, Hamza Salim, Basel Musmar, Nicole M Cancelliere, Christoph J. Griessenauer, Robert W. Regenhardt, Jesse Jones, Vincent M. Tutino, Zuha Hasan, Nicola Limbucci, Sovann V. Lay, Julian Spears, James D. Rabinov, Mark R. Harrigan, Adnan H. Siddiqui, Elad I. Levy, Christopher J. Stapleton, Leonardo Renieri, Christophe Cognard, Hamza Shaikh, Anna Luisa Kühn, Markus Möhlenbruch, Stavropoula I Tjoumakaris, Pascal Jabbour, Philipp Taussky, Fabio Settecase, Manraj K. S. Heran, Anh Nguyên, David Volders, Pablo Harker, Diego A. Devia, Ajit S. Puri, Marios Psychogios, Juan C. Puentes, Giuseppe Leone, Giuseppe Buono, Margherita Tarantino, Mario Muto, Francesco Briganti, Shamsher Dalal, Vamsi Gontu, Rodolfo E. Alcedo Guardia, Juan C. Vicenty‐Padilla, Patrick A. Brouwer, Matthias H. Schmidt, Clemens M. Schirmer, Gwynedd E. Pickett, Tommy Andersson, Michael Söderman, Thomas R. Marotta, Hugo H. Cuellar-Saenz, Ajith J. Thomas, Aman B. Patel, Vítor Mendes Pereira, Nimer Adeeb

Bibliographic record

VenueClinical Neuroradiology · 2024
Typearticle
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsVancouver General HospitalUniversity of British ColumbiaSt. Michael's Hospital
Fundersnot available
KeywordsFlow diverterMedicineMulticenter studyRetrospective cohort studyRadiologyEndovascular treatmentAneurysmSurgeryRandomized controlled trial

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.004
metaresearch head score (Gemma)0.008
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 score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.067
GPT teacher head0.380
Teacher spread0.313 · 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

Citations9
Published2024
Admission routes1
Has abstractno

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