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Record W2509152721 · doi:10.1097/ana.0000000000000350

What Next After GAS and PANDA?

2016· article· en· W2509152721 on OpenAlexaff
Caleb Ing, Virginia Rauh, David O. Warner, Lena S. Sun

Bibliographic record

VenueJournal of Neurosurgical Anesthesiology · 2016
Typearticle
Languageen
FieldNeuroscience
TopicAnesthesia and Neurotoxicity Research
Canadian institutionsColumbia College
FundersAgency for Healthcare Research and Quality
KeywordsMedicineObservational studyAnesthesiologyPerioperativeClinical trialIntensive care medicineNeuropsychologyAnestheticMEDLINEPsychiatryAnesthesiaCognitionInternal medicine

Abstract

fetched live from OpenAlex

On April 16 and 17, 2016, the Fifth biennial Pediatric Anesthesia & Neurodevelopment Assessment (PANDA) symposium was convened at the Morgan Stanley Children's Hospital of New York at Columbia University Medical Center. During the symposium, experts in the fields of anesthesiology, neuropsychology, and epidemiology were convened in a small group session to determine the level of confidence in the current clinical evidence and the next steps in anesthetic neurotoxicity clinical research. Among the participants in the discussion, there remained a lack of consensus on whether anesthetic exposure causes long-term neurodevelopmental deficits in children based on the current evidence. This causal relationship between anesthesia exposure and neurodevelopmental deficit is difficult to establish using observational data, and current and future clinical trials are critical for answering this question. It was, however, recognized that the continuum of data that is seen in studies of other toxic environmental exposures, such as lead poisoning, has not been established in the anesthetic neurotoxicity literature, specifically regarding the timing of the exposure, the dose effects, contributing perioperative conditions, or vulnerable populations. As a result, these questions may need to be addressed in observational studies to guide future clinical trials.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.049
GPT teacher head0.289
Teacher spread0.239 · 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 designBench or experimental
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

Citations7
Published2016
Admission routes1
Has abstractyes

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