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Record W2022273963 · doi:10.1111/pan.12007

New technologies in pediatric anesthesia

2012· review· en· W2022273963 on OpenAlexaff
Helen Holtby, Justin Skowno, Daryl J. Kor, Randall P. Flick, Shoichi Uezono

Bibliographic record

VenuePediatric Anesthesia · 2012
Typereview
Languageen
FieldMedicine
TopicHemodynamic Monitoring and Therapy
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicinePulse oximetryPerioperativeIntensive care medicineVenipunctureBlood managementPsychological interventionCardiac surgeryAnesthesiaSurgery

Abstract

fetched live from OpenAlex

This article reviews potential pediatric applications of 3 new technologies. (1) Pulse oximetry-based hemoglobin determination: Hemoglobin determination using spectrophotometric methods recently has been introduced in adults with varied success. This non-invasive and continuous technology may avoid venipuncture and unnecessary transfusion in children undergoing surgery with major blood loss, premature infants undergoing unexpected and complicated emergency surgery, and children with chronic illness. (2) Continuous cardiac output monitoring: In adults, advanced hemodynamic monitoring such as continuous cardiac output monitoring has been associated with better surgical outcomes. Although it remains unknown whether similar results are applicable to children, current technology enables the monitoring of cardiac output non-invasively and continuously in pediatric patients. It may be important to integrate the data about cardiac output with other information to facilitate therapeutic interventions. (3) Anesthesia information management systems: Although perioperative electronic anesthesia information management systems are gaining popularity in operating rooms, their potential functions may not be fully appreciated. With advances in information technology, anesthesia information management systems may facilitate bedside clinical decisions, administrative needs, and research in the perioperative setting.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, 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 score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.037
GPT teacher head0.311
Teacher spread0.274 · 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

Citations35
Published2012
Admission routes1
Has abstractyes

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