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Patient Selection in a Pragmatic Study on the Management of Patients with Brain Arteriovenous Malformations
Jean Raymond, Anass Benomar, Jean‐Christophe Gentric, Lorena Nico, Emma Bacchus, Ruby Klink, Daniela Iancu, Alain Weill, Daniel Roy, I. Pélissou-Guyotat, Sorin Aldéa, Charlotte Barbier, Thomas Gaberel, Chrysanthi Papagiannaki, Stéphane Derrey, Thanh N. Nguyen, Mohamad Abdalkader, Christophe Cognard, Anne-Christine Januel, Jean-François Sabatier, Vincent Jecko, Vincent Costalat, Marine Le Corre, Jean‐Yves Gauvrit, Xavier Morandi, Hubert Desal, Romain Bourcier, Rémy Beaujeux, François Proust, Geraldine Viard, Guylaine Gévry, Tim E. Darsaut, Jean Raymond, Cian O′Kelly, Michael Chow, J. Max Findlay, Jeremy Rempel, Robert Fahed, Howard Lesiuk, Brian Drake, Michel Nonent, Julien Ognard, Mourad Cheddad El Aouni, Romuald Seizeur, Serge Timsit, Olivier Pradier, Romain Boursier, F. Thillays, Vincent Roualdes, Raphaël Blanc, Lionel Calvière, J.-Y. Gauvrit, Hélène Raoult, François Eugene, Anthony Le Bras, Jean‐Christophe Ferré, Christophe Paya, Isabelle Lecouillard, Elodie Nouhaud, Thomas Ronzière, D. Trystram, Olivier Naggara, Christine Rodriguez-Régent, Basile Kerleroux, Évelyne Emery, Emmanuel Touzé, Roberto Riva, Isabelle Pellisou-Guyotat, Jacques Guyotat, Monsef Berhouma, Chloé Dumot, Nassim Bougaci, Guillaume Charbonnier, Serge Bracard, René Anxionnat, Benjamin Gory, Thierry Civit, Valérie Bernier‐Chastagner, Gaultier Marnat, Guillaume Penchet, Edouard Gimbert, Aymeri Huchet, Denis Herbreteau, Grégoire Boulouis, Richard Bibi, Héloïse Ifergan, Kévin Janot, Stéphane Velut, Hervé Brunel, Pièrre-Hugues Roche, Thomas Graillon, Hadrien Peyrière, Jean-Marc Kaya, Adamou Touta, Lucas Troude, Sébastien Boissonneau, Frédéric Clarençon, Eimad Shotar, Nader Sourour, Stéphanie Lenck, Kévin Premat, Anne‐Laure Boch, Philippe Cornu, Aurélien Nouet, Vincent Costalat, Alain Bonafé, Cyril Dargazanli, Gregory Gascou, Pierre-Henri Lefevre, Carlos Riquelme, Raoul Pop, H. Cébula, Irène Ollivier, Giorgio Spatola, Laurent Spell, Vanessa Chalumeau, Sophie Gallas, Léon Ikka, Cristian Mihalea, Augustin Ozanne, Jildaz Caroff, Emmanuel Chabert, Charbel Mounayer, Aymeric Rouchaud, François Caire, F. Ricolfi, Pierre Thouant, Catherine Cao, Klaus-Luc Mourier, Walid Farah, Thien Huynh, Rabih G. Tawk, Andrew P. Carlson, Luciana Alves Oliveira Silva, Nayara de Lima Froio, Gisele Sampaio Silva, Francisco Mont’Alverne, Jose Luri Martins, George Nilton Nunes Mendes, Rodrigo Rivera Miranda
Bibliographic record
VenueWorld Neurosurgery · 2023
Typearticle
Languageen
FieldMedicine
TopicVascular Malformations Diagnosis and Treatment
Canadian institutionsUniversité du Québec à MontréalUniversity of Alberta HospitalHealth Sciences CentreCentre Hospitalier de l’Université de Montréal
FundersMedtronic
KeywordsMedicineArteriovenous malformationIntracranial Arteriovenous MalformationsSelection (genetic algorithm)RadiologyCerebral angiographyAngiographyArtificial intelligence
Abstract
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.018
Threshold uncertainty score0.271
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 head0.014
GPT teacher head0.237
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation status
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 teacher head, 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
Citations2
Published2023
Admission routes1
Has abstractno