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Record W3137789819 · doi:10.1111/ijcp.14160

Intraoperative neurophysiological monitoring in paediatric neurosurgery

2021· review· en· W3137789819 on OpenAlexaff
Prasanna Udupi Bidkar, Astha Thakkar, Nitin Manohar, Keerthi Rao

Bibliographic record

VenueInternational Journal of Clinical Practice · 2021
Typereview
Languageen
FieldMedicine
TopicIntraoperative Neuromonitoring and Anesthetic Effects
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineIntraoperative neurophysiological monitoringNeurosurgeryPopulationNeurophysiologyIncidence (geometry)Neurological problemsSurgeryPediatrics

Abstract

fetched live from OpenAlex

Intraoperative neurophysiological monitoring (IONM) is commonly used in various surgical procedures in adults, but with technological and anaesthetic advancements, its use has extended to the paediatric population. The use of IONM in children poses a unique set of challenges considering the anatomical and physiological differences in this group of patients. The use of IONM aids in the localization of neural structures and enables surgeons to preserve the functional neural structures leading to decreased incidence of postoperative neurological deficits and better patient outcomes. In this article, we review the use of IONM in paediatric patients undergoing various spinal and cranial neurosurgical procedures. We discuss the patient characteristics, type of surgeries, and technical and anaesthetic considerations about IONM in this population.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.175
GPT teacher head0.535
Teacher spread0.360 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations17
Published2021
Admission routes1
Has abstractyes

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Same venueInternational Journal of Clinical PracticeSame topicIntraoperative Neuromonitoring and Anesthetic EffectsFrench-language works237,207