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Record W4384342340 · doi:10.1111/pan.14726

Perioperative management of infant inguinal hernia surgery; a review of the recent literature

2023· review· en· W4384342340 on OpenAlexaff
Fiona Taverner, Prakash Krishnan, Robert Baird, Britta S. von Ungern‐Sternberg

Bibliographic record

VenuePediatric Anesthesia · 2023
Typereview
Languageen
FieldNeuroscience
TopicAnesthesia and Neurotoxicity Research
Canadian institutionsBC Children's HospitalUniversity of British Columbia Hospital
FundersNational Health and Medical Research CouncilStan Perron Charitable Foundation
KeywordsMedicineInguinal herniaPerioperativeGeneral surgeryHerniaAirwayInguinal hernia surgeryIntensive care medicineSurgery

Abstract

fetched live from OpenAlex

Inguinal hernia surgery is one of the most common electively performed surgeries in infants. The common nature of inguinal hernia combined with the high-risk population involving a predominance of preterm infants makes this a particular area of interest for those concerned with their perioperative care. Despite a large volume of literature in the area of infant inguinal hernia surgery, there remains much debate amongst anesthetists, surgeons and neonatologists regarding the optimal perioperative management of these patients. The questions asked by clinicians include; when should the surgery occur, how should the surgery be performed (open or laparoscopic), how should the anesthesia be conducted, including regional versus general anesthesia and airway devices used, and what impact does anesthesia choice have on the developing brain? There is a paucity of evidence in the literature on the concerns, priorities or goals of the parents or caregivers but clearly their opinions do and should matter. In this article we review the current clinical surgical and anesthesia practice and evidence for infants undergoing inguinal hernia surgery to help clinicians answer these questions.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.110
GPT teacher head0.365
Teacher spread0.255 · 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 designSystematic review
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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