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Record W4317556120 · doi:10.1097/ana.0000000000000904

Multimodal Analgesia and Intraoperative Neuromonitoring

2023· review· en· W4317556120 on OpenAlex
Kan Ma, John F. Bebawy, Laura B. Hemmer

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Neurosurgical Anesthesiology · 2023
Typereview
Languageen
FieldMedicine
TopicIntraoperative Neuromonitoring and Anesthetic Effects
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineModalitiesMultimodal therapyAnestheticIntensive care medicineAnesthesiaIntraoperative AwarenessSurgery

Abstract

fetched live from OpenAlex

Intraoperative neuromonitoring has been a valuable tool for ensuring the functional integrity of vital neural structures by providing real-time feedback to the operative team during procedures where neurological structures are at risk. Commonly used intravenous and inhaled anesthetic drugs are known to affect waveform parameters measured with various intraoperative neuromonitoring modalities. While the concept of opioid-sparing multimodal analgesia has gained popularity in recent years, the impact of such a strategy on intraoperative neuromonitoring remains poorly characterized, in contrast to the more well-established concepts and literature regarding the effects of other hypnotic agents on neuromonitoring quality. The purpose of this focused review is to provide an overview of the clinical evidence pertaining to the pharmacological interaction of certain multimodal analgesics with routine intraoperative neuromonitoring modalities.

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.

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)0.001
metaresearch head score (Gemma)0.001
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: Review
Teacher disagreement score0.887
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.075
GPT teacher head0.375
Teacher spread0.300 · 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