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Record W3023185520 · doi:10.1542/neo.21-5-e298

Pathogenesis and Management of Indirect Hyperbilirubinemia in Preterm Neonates Less Than 35 Weeks: Moving Toward a Standardized Approach

2020· review· en· W3023185520 on OpenAlexaff
Anish Pillai, Aakash Pandita, Horacio Osiovich, Deepak Manhas

Bibliographic record

VenueNeoReviews · 2020
Typereview
Languageen
FieldMedicine
TopicNeonatal Health and Biochemistry
Canadian institutionsB.C. Women's Hospital & Health CentreUniversity of British Columbia
Fundersnot available
KeywordsMedicineExchange transfusionPediatricsIncidence (geometry)PopulationIntensive care medicine

Abstract

fetched live from OpenAlex

Premature infants have a higher incidence of indirect hyperbilirubinemia than term infants. Management of neonatal indirect hyperbilirubinemia in late preterm and term neonates has been well addressed by recognized, consensus-based guidelines. However, the extension of these guidelines to the preterm population has been an area of uncertainty because of limited evidence. This leads to variation in clinical practice and lack of recognition of the spectrum of bilirubin-induced neurologic dysfunction (BIND) in this population. Preterm infants are metabolically immature and at higher risk for BIND at lower bilirubin levels than their term counterparts. Early use of phototherapy to eliminate BIND and minimize the need for exchange transfusion is the goal of treatment in premature neonates. Although considered relatively safe, phototherapy does have side effects, and some NICUs tend to overuse phototherapy. In this review, we describe the epidemiology and pathophysiology of BIND in preterm neonates, and discuss our approach to standardized management of indirect hyperbilirubinemia in the vulnerable preterm population. The proposed treatment charts suggest early use of phototherapy in preterm neonates with the aim of reducing exposure to high irradiance levels, minimizing the need for exchange transfusions, and preventing BIND. The charts are pragmatic and have additional curves for stopping phototherapy and escalating its intensity. Having a standardized approach would support future research and quality improvement initiatives that examine dose and duration of phototherapy exposure with relation to outcomes.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.074
GPT teacher head0.342
Teacher spread0.268 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations23
Published2020
Admission routes1
Has abstractyes

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