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Record W4411735381 · doi:10.1016/j.jocn.2025.111411

Impact of smoking on occlusion rates following stereotactic radiosurgery for Spetzler Martin grade I-III brain arteriovenous malformations – A propensity score matched analysis of the MISTA consortium

2025· article· en· W4411735381 on OpenAlexaff
Daniel Sconzo, Felipe Ramirez‐Velandia, Sandeep Muram, Alejandro Enríquez-Marulanda, Coleman P. Riordan, Nimer Adeeb, Basel Musmar, Hamza Salim, Sandeep Kandregula, Adam A Dmywtriw, Ahmed Abdelsalam, Cagdas Ataoglu, Ufuk Erginoğlu, Douglas Kondziolka, Assala Aslan, Kareem El Naamani, Jason Sheehan, Min Park, Hussein A. Zeineddine, Natasha Ironside, Deepak Kumbhare, Sanjeev Gummadi, Muhammed Amir Essibayi, Salem M Tos, Abdullah Keleş, Arwin Rezai, Johannes Pöppe, Rajeev Sen, Mustafa K. Başkaya, Christoph J. Griessenauer, Pascal Jabbour, Stavropoula I Tjoumakaris, Elias Atallah, Howard A. Riina, Abdallah Abushehab, Jan‐Karl Burkhardt, Robert M. Starke, Laligam N. Sekhar, Michael R Levitt, David Altschul, Neil Haranhalli, Malia McAvoy, Adib A. Abla, Christopher Stapleton, Matthew J. Koch, Visish M Srinivasan, Peng R. Chen, Spiros Blackburn, Louis J. Kim, Omar Choudhri, Bryan Pukenas, Georgios Mantziaris, Sean O’Leary, Peter Kan, Yanlin Li, Davide Simonato, Ketan R. Bulsara, Maurizio Fuschi, Ali Alaraj, Şahin Hanalıoğlu, Aman B. Patel, Amey Savardekar, Hugo Cuellar, Michael T. Lawton, Jacques J. Morcos, Bharat Guthikonda, Philipp Taussky, Christopher S. Ogilvy

Bibliographic record

VenueJournal of Clinical Neuroscience · 2025
Typearticle
Languageen
FieldMedicine
TopicVascular Malformations Diagnosis and Treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineRadiosurgeryPropensity score matchingIntracranial Arteriovenous MalformationsArteriovenous malformationOcclusionInternal medicineRadiologyCerebral angiographyAngiography

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 distilled prediction

Teacher imitation

Not 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)0.002
metaresearch head score (Gemma)0.004
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 score0.004
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.106
GPT teacher head0.420
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 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

Citations0
Published2025
Admission routes1
Has abstractno

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