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Record W3137789819 · doi:10.1111/ijcp.14160

Intraoperative neurophysiological monitoring in paediatric neurosurgery

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

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

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.

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)0.048
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.048
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.004
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 head0.175
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