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Record W2513976017 · doi:10.1097/ana.0000000000000355

Use of Anesthesia for Imaging Studies and Interventional Procedures in Children

2016· article· en· W2513976017 on OpenAlexaff
Yolanda Y. Huang, Lucy Li, M Monteleone, Lynne R. Ferrari, Lisa J. States, James J. Riviello, Steven G. Kernie, Ali Mencin, Sumit Gupta, Lena S. Sun

Bibliographic record

VenueJournal of Neurosurgical Anesthesiology · 2016
Typearticle
Languageen
FieldNeuroscience
TopicAnesthesia and Neurotoxicity Research
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineAnesthesiologySedationAnestheticNeurologyAnesthesiaIntensive care medicineGeneral surgeryPsychiatry

Abstract

fetched live from OpenAlex

Ongoing investigation from the Pediatric Anesthesia NeuroDevelopment Assessment (PANDA) study hopes to examine the long-term effect on cognitive and language development of a single anesthetic exposure in children undergoing inguinal hernia repair. The fifth PANDA Symposium, held in April 2016, continued the mission of previous symposia to examine evidence from basic science and clinical studies on potential neurotoxic effects of anesthetics on developing brain. At the 2016 Symposium, a panel of specialists from nonsurgical pediatric disciplines including anesthesiology, radiology, neurology, gastroenterology, oncology, cardiology, and critical care reviewed use of anesthesia in their practices, including how concern over possible neurodevelopmental effects of early childhood anesthetic exposure has changed discussion with patients and families regarding risks and benefits of imaging studies and interventional procedures involving sedation or anesthesia. This paper summarizes presentations from nonsurgical pediatric specialists at the 2016 PANDA Symposium.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.094
GPT teacher head0.347
Teacher spread0.253 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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