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Record W4283760639 · doi:10.1542/neo.23-7-e486

Multimodal Assessment of Systemic Blood Flow in Infants

2022· article· en· W4283760639 on OpenAlex
Aimann Surak, Renjini Lalitha, Eyad Bitar, Abbas Hyderi, Matthew Hicks, Po‐Yin Cheung, Kumar Kumaran

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.

Bibliographic record

VenueNeoReviews · 2022
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsUniversity of AlbertaLondon Health Sciences CentreStollery Children's Hospital
Fundersnot available
KeywordsMedicineIntensive care medicineReliability (semiconductor)PopulationClinical PracticeBlood flowRisk analysis (engineering)CardiologyPhysical therapy

Abstract

fetched live from OpenAlex

The assessment of systemic blood flow is a complex and comprehensive process with clinical, laboratory, and technological components. Despite recent advancements in technology, there is no perfect bedside tool to quantify systemic blood flow in infants that can be used for clinical decision making. Each option has its own merits and limitations, and evidence on the reliability of these physiology-based assessment processes is evolving. This article provides an extensive review of the interpretation and limitations of methods to assess systemic blood flow in infants, highlighting the importance of a comprehensive and multimodal approach in this population.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.909
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.057
GPT teacher head0.425
Teacher spread0.368 · 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