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Record W4312084950 · doi:10.1016/j.chest.2022.12.010

Operational Definitions Related to Pediatric Ventilator Liberation

2022· article· en· W4312084950 on OpenAlexaff
Samer Abu‐Sultaneh, Narayan P. Iyer, Analía Fernández, Michael Gaies, Sebastián González‐Dambrauskas, Justin Hotz, Martin C. J. Kneyber, Yolanda M. López‐Fernández, Alexandre T. Rotta, David K. Werho, Arun Baranwal, Bronagh Blackwood, Hannah J. Craven, Martha A. Q. Curley, Sandrine Essouri, José Roberto Fioretto, Silvia Hartmann, Philippe Jouvet, Steven Kwasi Korang, Gerrard F. Rafferty, Padmanabhan Ramnarayan, Louise Rose, Lyvonne N. Tume, Elizabeth C. Whipple, Judith Ju‐Ming Wong, Guillaume Émériaud, Christopher W. Mastropietro, Natalie Napolitano, Christopher J. L. Newth, Robinder G. Khemani

Bibliographic record

VenueCHEST Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersSchool of Medicine, Indiana UniversityEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Heart, Lung, and Blood InstituteNational Institute for Health and Care ResearchEli Lilly and Company
KeywordsMedicinePsychological interventionVotingIntensive care medicineMechanical ventilationNursingPolitical scienceLaw

Abstract

fetched live from OpenAlex

BACKGROUND: Common, operational definitions are crucial to assess interventions and outcomes related to pediatric mechanical ventilation. These definitions can reduce unnecessary variability among research and quality improvement efforts, to ensure findings are generalizable, and can be pooled to establish best practices. RESEARCH QUESTION: Can we establish operational definitions for key elements related to pediatric ventilator liberation using a combination of detailed literature review and consensus-based approaches? STUDY DESIGN AND METHODS: A panel of 26 international experts in pediatric ventilator liberation, two methodologists, and two librarians conducted systematic reviews on eight topic areas related to pediatric ventilator liberation. Through a series of virtual meetings, we established draft definitions that were voted upon using an anonymous web-based process. Definitions were revised by incorporating extracted data gathered during the systematic review and discussed in another consensus meeting. A second round of voting was conducted to confirm the final definitions. RESULTS: In eight topic areas identified by the experts, 16 preliminary definitions were established. Based on initial discussion and the first round of voting, modifications were suggested for 11 of the 16 definitions. There was significant variability in how these items were defined in the literature reviewed. The final round of voting achieved ≥ 80% agreement for all 16 definitions in the following areas: what constitutes respiratory support (invasive mechanical ventilation and noninvasive respiratory support), liberation and failed attempts to liberate from invasive mechanical ventilation, liberation from respiratory support, duration of noninvasive respiratory support, total duration of invasive mechanical ventilation, spontaneous breathing trials, extubation readiness testing, 28 ventilator-free days, and planned vs rescue use of post-extubation noninvasive respiratory support. INTERPRETATION: We propose that these consensus-based definitions for elements of pediatric ventilator liberation, informed by evidence, be used for future quality improvement initiatives and research studies to improve generalizability and facilitate comparison.

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)0.007
metaresearch head score (Gemma)0.021
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: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.021
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.004
Science and technology studies0.0020.010
Scholarly communication0.0080.007
Open science0.0020.004
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0060.002

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 head0.040
GPT teacher head0.277
Teacher spread0.237 · 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
GenreEmpirical

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
Published2022
Admission routes1
Has abstractno

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