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Record W4210832136 · doi:10.1111/apa.16280

Utilisation of neonatologist‐performed echocardiography in shock among neonatologists with interest in haemodynamic: International survey

2022· article· en· W4210832136 on OpenAlexaboutno aff
Virginie Meau‐Petit, Corinne Levy, Isabelle Guellec

Bibliographic record

VenueActa Paediatrica · 2022
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNeonatologyMedicineShock (circulatory)HemodynamicsEmergency medicinePediatricsIntensive care medicineCardiologyInternal medicine

Abstract

fetched live from OpenAlex

AIM: Neonatologist-performed echocardiography (NPE) is recommended during shock. We aimed to assess factors associated with NPE utilisation in the NICU and physiological information obtained during management of shock. METHODS: An Internet-based survey, sent to neonatologists with interest in haemodynamics, studying NPE utilisation in shock management through a real clinical case and correlating its use with responders' training and NICU settings. RESULTS: Fifty-nine completed surveys were received from the United Kingdom: 38%, Western Europe: 32%, Canada: 23% and other countries: 7%. Whilst managing the given clinical case, 90% of responders expected first NPE to exclude congenital heart disease-although only 61% could exclude it confidently (71% in trained clinicians vs. 29% without training; p < 0.01). NPE utilisation prior to initiate treatment was significantly correlated with mean number of neonatologists able to perform NPE in the NICU (4.9 vs. 2.9 neonatologists per unit; p = 0.02). Similarly, for ongoing therapeutic guidance, NPE was more used in trained clinicians (p < 0.01). 88% and 81% of responders studied a combination of multiple parameters to assess filling and systemic flow, respectively. CONCLUSION: Neonatologist-performed echocardiography during shock management differs with previous training and number of doctors able to perform echocardiography in NICU. This study highlighted the need for enhanced training implementation.

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)0.000
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 score0.005
Threshold uncertainty score0.650

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.269
Teacher spread0.238 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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