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Record W2792039244 · doi:10.3389/fneur.2018.00177

Postoperative Delirium, Learning, and Anesthetic Neurotoxicity: Some Perspectives and Directions

2018· article· en· W2792039244 on OpenAlexaff
W. Alan C. Mutch, Renée El-Gabalawy, M. Ruth Graham

Bibliographic record

VenueFrontiers in Neurology · 2018
Typearticle
Languageen
FieldNeuroscience
TopicAnesthesia and Neurotoxicity Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNeurotoxicityDeliriumAnestheticMedicineIsofluranePerioperativeCognitionIntensive care medicineAnesthesiaNeurosciencePsychologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Evidence of anesthetic neurotoxicity is unequivocal when studied in animal models. These findings have translated poorly to the clinical domain when equated to postoperative delirium (POD) in adults and postoperative cognitive dysfunction (POCD) in either children or the elderly. In this perspective, we examine various reasons for the differences between animal modeling of neurotoxicity and the clinical situation of POD and POCD and make suggestions as to potential directions for ongoing research. We hypothesize that the animal anesthetic neurotoxicity models are limited, in part, due to failed scaling correction of physiological time. We posit that important insights into POCD in children and adults may be gleaned from studies in adults examining alterations in perioperative management designed to limit POD. In this way, POD may be more useful as the proxy for POCD rather than neuronal dropout or behavioral abnormalities that have been used in animal models but which may not be proxies for the human condition. We argue that it is time to move beyond animal models of neurotoxicity to directly examine these problems in well-conducted clinical trials with comprehensive preoperative neuropsychometric and psychiatric testing, high fidelity intraoperative monitoring of physiological parameters during the anesthetic course and postoperative assessment of subthreshold and full classification of POD. In this manner, we can "model ourselves" to better understand these important and poorly understood conditions.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score0.773

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.278
Teacher spread0.263 · 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

Citations29
Published2018
Admission routes1
Has abstractyes

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