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Record W4311545805 · doi:10.1097/ana.0000000000000882

Impact of Anesthetic Exposures on the Neurocognitive Profiles of Pediatric Brain Tumor Survivors: A New Direction for Research and Multidisciplinary Collaboration

2022· review· en· W4311545805 on OpenAlexaff
Lisa Eisler, William M. Jackson, Lena S. Sun, Stephen A. Sands

Bibliographic record

VenueJournal of Neurosurgical Anesthesiology · 2022
Typereview
Languageen
FieldNeuroscience
TopicAnesthesia and Neurotoxicity Research
Canadian institutionsColumbia College
FundersNational Center for Advancing Translational SciencesNational Cancer Institute
KeywordsNeurocognitiveMedicineAnestheticNeurotoxicityBrain tumorMultimodal therapyMultidisciplinary approachPediatricsAnesthesiaIntensive care medicineCognitionPsychiatrySurgeryInternal medicineToxicity

Abstract

fetched live from OpenAlex

Primary brain tumors are the most commonly diagnosed solid tumors in children, and pediatric brain tumor survivors experience lasting, pervasive deficits of neurocognitive functioning. Repeated exposure to anesthetic drugs is a necessary component not only of surgical resection but also of multimodal cancer care for the youngest patients with brain tumors. The potential for anesthetic neurotoxicity to worsen neurocognitive outcomes in this vulnerable group, therefore, warrants our attention and further study through multi-disciplinary collaboration. This review discusses neurocognitive functioning in pediatric brain tumor survivors, highlighting the findings of a recent study of children with tumors of the posterior fossa which identified treatment-related risk factors for neurocognitive difficulties, with those undergoing multimodal therapies (eg, chemotherapy and irradiation) experiencing the greatest deficits compared with healthy controls. The role of anesthetic neurotoxicity in long-term outcomes among pediatric brain tumor survivors is also reviewed.

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)0.003
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.819
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.242
GPT teacher head0.452
Teacher spread0.210 · 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.

Study designOther design
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

Citations1
Published2022
Admission routes1
Has abstractyes

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