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Record W2412905282 · doi:10.7863/ultra.15.06037

Use of Targeted Neonatal Echocardiography and Focused Cardiac Sonography in Tertiary Neonatal Intensive Care Units

2016· article· en· W2412905282 on OpenAlexaff
Amit Mukerji, Yenge Diambomba, Shoo K. Lee, Amish Jain

Bibliographic record

VenueJournal of Ultrasound in Medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsMount Sinai HospitalUniversity of TorontoMcMaster UniversityMcMaster Children's Hospital
Fundersnot available
KeywordsMedicineIntensive care medicineNeonatal intensive care unitCardiac UltrasoundModalitiesPsychological interventionHemodynamicsIntensive careCardiac dysfunctionIntensive care unitCardiac function curveRadiologyCardiologyUltrasoundPediatricsHeart failure

Abstract

fetched live from OpenAlex

Focused cardiac sonography and targeted neonatal echocardiography refer to goal-directed cardiac imaging using ultrasound, typically by noncardiologic specialists. Although the former consists of a rapid qualitative assessment of cardiac function, which is usually performed by acute care practitioners, the latter refers to detailed functional echocardiography to obtain quantitative and qualitative indexes of pulmonary and systemic hemodynamics in sick neonates and is typically performed by neonatologists. Although the use of these modalities is increasing, they still remain unavailable in most North American centers providing acute care to neonates, partly because of limited data regarding their direct impact on patient care. Here we present a series of 5 cases from a large perinatal unit in which immediate availability of relevant expertise led to important and arguably life-saving clinical interventions. In 4 of these cases, focused cardiac sonography was sufficient to make the diagnosis, whereas in 1 case, clinical integration of detailed systemic hemodynamics measured on target neonatal echocardiography was required.

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)0.001
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation 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 score0.023
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
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 head0.030
GPT teacher head0.297
Teacher spread0.267 · 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.

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

Citations16
Published2016
Admission routes1
Has abstractyes

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