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Record W3034870573 · doi:10.1093/pch/pxaa033

Routine imaging of the preterm neonatal brain

2020· review· en· W3034870573 on OpenAlexaff
M Guillot, Vann Chau, Brigitte Lemyre

Bibliographic record

VenuePaediatrics & Child Health · 2020
Typereview
Languageen
FieldMedicine
TopicNeonatal and fetal brain pathology
Canadian institutionsCanadian Paediatric Society
Fundersnot available
KeywordsNeuroimagingMedicinePediatricsPsychiatry

Abstract

fetched live from OpenAlex

Routine brain imaging to detect injuries affecting preterm infants is used to predict long-term outcomes and identify complications that might necessitate an intervention. Although magnetic resonance imaging may be indicated in some specific cases, head ultrasound is the most widely used technique and, because of portability and ease of access, is the best modality for routine imaging. Routine head ultrasound examination is recommended for all infants born at or before 31+6 weeks gestation. For preterm neonates born between 32+0 to 36+6 weeks gestation, routine head ultrasound is recommended only in presence of risk factors for intracranial hemorrhage or ischemia. Brain imaging in the first 7 to 14 days postbirth is advised to detect most germinal matrix and intraventricular hemorrhages. Repeat imaging at 4 to 6 weeks of age is recommended to detect white matter injury.

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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.020
GPT teacher head0.310
Teacher spread0.290 · 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

Citations67
Published2020
Admission routes1
Has abstractyes

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