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Record W3011401629 · doi:10.1093/tropej/fmaa012

The BCPAP Score: Five Questions to Assess the Effectiveness of a Bubble CPAP Circuit

2020· article· en· W3011401629 on OpenAlex
Stephen C. John, Eric Cheng, Sunil P John

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Tropical Pediatrics · 2020
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsEngineers Without Borders Canada
Fundersnot available
KeywordsMedicineContinuous positive airway pressureBubbleIntensive care medicineRespiratory physiologyRespiratory distressPediatricsAnesthesiaRespiratory systemInternal medicineComputer scienceObstructive sleep apnea

Abstract

fetched live from OpenAlex

Respiratory illnesses are a leading cause of infant mortality worldwide. Bubble CPAP is a simple and effective treatment for infants in respiratory distress. Across resource-limited settings, various bubble CPAP setups have been used with widely varying results. Based on fundamental fluid dynamics principles and clinical experience, the BCPAP score has been developed to gauge effectiveness of bubble CPAP delivery in different settings. Five questions addressing Bubbles, Circuit, Prongs, Airway and Pressure allow clinicians to rapidly determine whether they are delivering effective bubble CPAP. This article describes how to calculate a BCPAP score and explains the rationale behind the BCPAP score.

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.

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)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.094
GPT teacher head0.388
Teacher spread0.294 · 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