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Cardiac arrest with pulseless electrical activity rhythm in newborn infants: a case series

2019· article· en· W2917997848 on OpenAlexafffundabout
Deandra Luong, Po‐Yin Cheung, Keith J. Barrington, Peter G. Davis, Jennifer Unrau, Shyamala Dakshinamurti, Georg M. Schmölzer

Bibliographic record

VenueArchives of Disease in Childhood Fetal & Neonatal · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsRoyal Alexandra HospitalUniversity of ManitobaCentre Hospitalier Universitaire Sainte-JustineUniversity of CalgaryUniversity of Alberta
FundersUniversity of AlbertaWomen and Children's Health Research InstituteNational Health and Medical Research CouncilHeart and Stroke Foundation of Canada
KeywordsPulseless electrical activityMedicineCardiopulmonary resuscitationNeonatal resuscitationResuscitationHeart rateCardiologyInternal medicineEmergency medicineBlood pressure

Abstract

fetched live from OpenAlex

The 2015 neonatal resuscitation guidelines added ECG to assess an infant's heart rate when determining the need for resuscitation at birth. However, a recent case report raised concerns about this technique in the delivery room. We report four cases of pulseless electrical activity during neonatal cardiopulmonary resuscitation in levels II-III neonatal intensive care units in Canada (Edmonton [n=3] and Winnipeg [n=1]).Healthcare providers should be aware that pulseless electrical activity can occur in newborn infants during cardiopulmonary resuscitation. We propose an adapted neonatal resuscitation algorithm to include pulseless electrical activity. Furthermore, in compromised newborns, heart rate should be assessed using a combination of methods/techniques to ensure accurate heart rate assessment. When ECG displays a heart rate but the infant is unresponsive, pulseless electrical activity should be suspected and chest compression should be started.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0030.003
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 head0.003
GPT teacher head0.227
Teacher spread0.223 · 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 designCase report
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

Citations39
Published2019
Admission routes3
Has abstractyes

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