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Record W4242134744 · doi:10.12788/3462

Should the Pendulum Swing Back? More Transfers to the ICU After Implementing Ward-Based High-Flow Nasal Cannula Initiation Protocols for Bronchiolitis

2020· article· en· W4242134744 on OpenAlexaff
Daniel J. Sklansky, Sanjay Mahant

Bibliographic record

VenueJournal of Hospital Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNasal cannulaMedicineBronchiolitisOxygen therapyAnesthesiaIntubationVentilation (architecture)Intensive care medicineWork of breathingCannulaMechanical ventilationSurgeryRespiratory systemInternal medicine

Abstract

fetched live from OpenAlex

As an appealing, physiologically plausible treatment, humidified oxygen delivery via high-flow nasal cannula (HFNC) has been rapidly adopted for the treatment of bronchiolitis despite weak evidence supporting its routine and early use in hypoxemic infants.1 Although HFNC use has been associated with decreased work of breathing and lower rates of progression to invasive ventilation in some studies, the one large trial published on the topic found no difference between early HFNC and standard oxygen therapy on length of stay in hospital, duration of oxygen therapy, or rates of intubation.2,3 No adequately powered studies have examined the effect of ward-based HFNC initiation on ICU transfer, an outcome that it is designed to prevent.

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)0.006
metaresearch head score (Gemma)0.039
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: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.039
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0240.002

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 head0.050
GPT teacher head0.334
Teacher spread0.284 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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