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Record W4387563631 · doi:10.1002/ppul.26721

Volume assured pressure support mode use for non‐invasive ventilation in pediatrics

2023· article· en· W4387563631 on OpenAlexaff
Nisha Cithiravel, Lena Xiao, Jenny Shi, Jackie Chiang, Reshma Amin

Bibliographic record

VenuePediatric Pulmonology · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience of respiration and sleep
Canadian institutionsInstitute for Clinical Evaluative SciencesSickKids FoundationUniversity of TorontoHospital for Sick Children
Fundersnot available
KeywordsMedicinePulmonologistsSpinal muscular atrophyHypoventilationIntensive care medicineNarcolepsyNeuromuscular diseasePediatricsDuchenne muscular dystrophyPhysical medicine and rehabilitationPhysical therapyDiseaseNeurologyRespiratory systemInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

There has been increasing interest in utilizing volume assured pressure support (VAPS) modes of ventilation for children, which historically had only been favored in adult populations. In addition to patients with obesity hypoventilation syndrome, newer pediatric populations for which it has recently been prescribed include congenital central hypoventilation syndrome and children with neuromuscular disease such as Duchenne muscular dystrophy and spinal muscular atrophy. Given its expanding use in pediatrics, greater familiarity with VAPS is essential for pediatric pulmonologists and sleep physicians. This review article will highlight methods of initiation for this mode, specific ventilator settings, discussion of suitable pediatric patient populations, ventilator titrations via formal polysomnograms and detailed ventilator data downloads specific interpretation. Finally, common challenges to be aware of and how to troubleshoot relevant machine alarms will be reviewed.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.046
GPT teacher head0.303
Teacher spread0.257 · 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 designObservational
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

Citations10
Published2023
Admission routes1
Has abstractyes

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