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Record W4318478995 · doi:10.3390/jpm13020260

Evaluation of Nociception during Pediatric Surgery: A Topical Review

2023· review· en· W4318478995 on OpenAlexaff
Gianluca Bertolizio, Marta Garbin, Pablo Ingelmo

Bibliographic record

VenueJournal of Personalized Medicine · 2023
Typereview
Languageen
FieldMedicine
TopicHead and Neck Surgical Oncology
Canadian institutionsMcGill UniversityUniversité de MontréalMcGill University Health CentreMontreal Children's Hospital
Fundersnot available
KeywordsMedicineNociceptionBioinformaticsInternal medicineBiology

Abstract

fetched live from OpenAlex

The association between intraoperative nociception and increased patient's morbidity is well established. However, hemodynamic parameters, such as heart rate and blood pressure, may result in an inadequate monitor of nociception during surgery. Over the last two decades, different devices have been marketed to "reliably" detect intraoperative nociception. Since the direct measure of nociception is impractical during surgery, these monitors measures nociception surrogates such as sympathetic and parasympathetic nervous systems responses (heart rate variability, pupillometry, skin conductance), electroencephalographic changes, and muscular reflex arc. Each monitor carries its own advantages and disadvantages. The manuscript aims to give an overview of the most up-to-date information available in the literature on current nociceptor monitors available in clinical practice, with particular focus on their applications in pediatrics.

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)0.012
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.739
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.297
GPT teacher head0.492
Teacher spread0.195 · 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 designNot applicable
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

Citations8
Published2023
Admission routes1
Has abstractyes

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