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Record W120233224 · doi:10.1177/159101991301900420

«Interventional Neuroradiology: A Neuroscience Sub-Specialty?»

2013· editorial· en· W120233224 on OpenAlex
Georges Rodesch, L Picard, Alex Berenstein, Alessandra Biondi, Serge Bracard, In Sup Choi, Feng Ling, Toshio Hyogo, David Lefeuvre, Marco Leonardi, Thomas E. Mayer, Shigeru Miyashi, Mario Muto, Ronie Leo Piske, Sirintara Pongpech, J. Reul, Michael Söderman, Dae Chul Suh, Donatella Tampieri, Allan Taylor, Karel G. terBrugge, Anton Valavanis, René van den Berg

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInterventional Neuroradiology · 2013
Typeeditorial
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsToronto Western HospitalMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsNeuroradiologyInterventional neuroradiologyMedicineMedical physicsSpecialtyNeuroscienceEngineering ethicsNeurologyRadiologyPathologyPsychiatryPsychology

Abstract

fetched live from OpenAlex

Interventional Neuroradiology (INR) is not bound by the classical limits of a speciality, and is not restricted by standard formats of teaching and education. Open and naturally linked towards neurosciences, INR has become a unique source of novel ideas for research, development and progress allowing new and improved approaches to challenging pathologies resulting in better anatomo-clinical results. Opening INR to Neurosciences is the best way to keep it alive and growing. Anchored in Neuroradiology, at the crossroad of neurosciences, INR will further participate to progress and innovation as it has often been in the past.

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.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.070
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.016
GPT teacher head0.291
Teacher spread0.275 · 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