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Record W3123210787 · doi:10.1111/jspn.12326

Who is using noninvasive ventilation? A descriptive study examining the population enrolled in a pediatric noninvasive ventilation program

2021· article· en· W3123210787 on OpenAlexaffabout
Mikelle Djkowich, Deborah Olmstead, Maria L. Castro‐Codesal, Shannon D. Scott

Bibliographic record

VenueJournal for Specialists in Pediatric Nursing · 2021
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsUniversity of AlbertaStollery Children's HospitalAlberta Health Services
Fundersnot available
KeywordsMedicineDescriptive statisticsPopulationNoninvasive ventilationPediatricsDescriptive researchFamily medicineEmergency medicineMechanical ventilationIntensive care medicineEnvironmental healthStatisticsPsychiatry

Abstract

fetched live from OpenAlex

DESIGN AND METHODS: This study used a retrospective design and involved reviewing the charts of infants and children enrolled in the noninvasive ventilation (NIV) program at a quaternary pediatric hospital located in Western Canada in 2017. Demographic and clinical variables were collected, along with variables related to adherence to NIV therapy. For data storage and analysis purposes, a comprehensive database was created. Descriptive statistics were used to analyze and better understand patterns within the data. RESULTS: Findings included a comprehensive description of the population of infants and children enrolled in this NIV program in 2017, including demographic and clinical variables as well as follow-up and adherence data. This study identified that the NIV program at this pediatric center has unique characteristics which provide an exciting opportunity for further research into the population that requires NIV support. PRACTICE IMPLICATIONS: This study presents new knowledge, gathered by examining the clinical characteristics of a pediatric population that requires NIV, which can be used to inform practice, support NIV program planning, and health resource allocation, as well as suggest directions for future research on pediatric NIV therapy.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.108
GPT teacher head0.437
Teacher spread0.329 · 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 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

Citations1
Published2021
Admission routes2
Has abstractyes

Explore more

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