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Record W4385520387 · doi:10.1055/s-0043-1771511

Pediatric Interventional Neuroradiology

2023· review· en· W4385520387 on OpenAlexaff
Flavio Requejo, Darío Teplisky, María Laura González Dutra, Diana Mariela Mouratian, Raghid Kikano, Thanh N. Nguyen, Mohamad Abdalkader

Bibliographic record

VenueSeminars in Neurology · 2023
Typereview
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsCégep de l'Abitibi Témiscamingue
Fundersnot available
KeywordsMedicineNeuroradiologyMedical physicsInterventional neuroradiologyRadiologyNeurologyPsychiatry

Abstract

fetched live from OpenAlex

Pediatric neurovascular disorders can lead to substantial mortality and morbidity if not diagnosed early and adequately managed. Children with neurovascular diseases cannot be treated as small adults as the vascular and central nervous system anatomy, physiology, and pathologies in children differ greatly from those of adults. In addition, some neurovascular pathologies are seen exclusively in children such as aneurysmal malformation of the vein of Galen, pial fistulas, and dural fistulas in the context of dural sinus disease. In this review, we aim to present an overview of the common pediatric neurovascular diseases along with their endovascular management.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0160.009

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 head0.058
GPT teacher head0.354
Teacher spread0.296 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2023
Admission routes1
Has abstractyes

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