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Record W4284971959 · doi:10.3233/npm-210974

Implementing a successful targeted neonatal echocardiography service and a training program: The ten stages of change

2022· review· en· W4284971959 on OpenAlex
Nadya Ben Fadel, Aimann Surak, Elham Almoli, Robert P. Jankov

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Neonatal-Perinatal Medicine · 2022
Typereview
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsMcMaster UniversityUniversity of AlbertaUniversity of Ottawa
Fundersnot available
KeywordsIntensive careProcess (computing)Service (business)MedicineHealth careProcess managementMedical emergencyIntensive care medicineOperations managementComputer scienceBusinessEngineeringPolitical science

Abstract

fetched live from OpenAlex

Implementing any new service or program in the health care system is not always straightforward; a multi-stage implementation process is required most of the time. With the advancements in neonatal care and increased survival rates, there has been an increased need for ongoing assessment of hemodynamic stability. At the Children's Hospital of Eastern Ontario and the Ottawa Hospital Neonatal Intensive Care Units (NICUs), University of Ottawa, Canada, Targeted Neonatal Echocardiography service (TnEcho) was successfully established and has led to improvement in the hemodynamic evaluation and decision making in neonatal intensive care. In this article, we describe our experience establishing this program and the process of ensuring its success. This review article highlights the ten steps taken by multiple stakeholders to achieve this goal; this may help other centres implement a similar program.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
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 head0.108
GPT teacher head0.380
Teacher spread0.272 · 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