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Record W2625758414 · doi:10.1093/pch/pxx091

Audit of pulse oximetry screening for critical congenital heart disease (CCHD) in newborns

2017· article· en· W2625758414 on OpenAlexaboutno aff
Marnie Lightfoot, Philip Hough, Alan Hudak, Michelle Gordon, Sarah Barker, Robert Meeder, Melanie Colpitts, Gwendolyn Roberts, William G. Smith

Bibliographic record

VenuePaediatrics & Child Health · 2017
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePulse oximetryAuditPediatricsNewborn screeningHeart diseaseOxygen saturationEmergency medicineAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To assess the efficacy of a new screening protocol for critical congenital heart disease (CCHD). BACKGROUND: In March 2014, the Ontario Provincial Council for Maternal Child Health (PCMCH) recommended screening for CCHD, utilizing pulse oximetry to measure oxygen saturation as part of the newborn examination. However, this is yet to be implemented in all hospitals. METHOD: An audit of consecutive healthy normal newborn patients in a secondary level centre in Ontario with early adoption of the screening recommendation over a 1-year period was undertaken. RESULTS: The median age of screening was 25 hours (6 to 80 hours). Compliance was 88% (95% if one excludes deliveries by a midwife as they did not agree to comply). Four patients screened positive and were seen by a paediatrician in consultation but did not have CCHD (specificity 99.4%). CONCLUSIONS: The current study shows that screening was successfully implemented in a Canadian hospital, with high specificity (99.4%) and good compliance (88%). Reasons for non-acceptance of screening by midwives need to be addressed.

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)0.032
metaresearch head score (Gemma)0.083
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.340

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.083
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.352
Teacher spread0.319 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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