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Record W2948481417 · doi:10.1002/uog.20272

ISUOG Practice Guidelines: ultrasound assessment of fetal biometry and growth

2019· article· en· W2948481417 on OpenAlexaff
Laurent Salomon, Žarko Alfirević, Fabrício da Silva Costa, Russell L. Deter, F. Figueras, T. Ghi, Phyllis Glanc, Asma Khalil, W. Lee, R. Napolitano, Aris T. Papageorghiou, Alexandros Sotiriadis, J. Stirnemann, Ants Toi, G. S. H. Yeo

Bibliographic record

VenueUltrasound in Obstetrics and Gynecology · 2019
Typearticle
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsMount Sinai HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineObstetricsGestational ageFetusIntrauterine growth restrictionBiophysical profilePregnancyCardiotocographySmall for gestational ageUltrasoundGestationFetal macrosomiaAmniotic fluidGestational diabetesRadiology

Abstract

fetched live from OpenAlex

INTRODUCTION These Guidelines aim to describe appropriate assessment of fetal biometry and diagnosis of fetal growth disorders. These disorders consist mainly of fetal growth restriction (FGR), also referred to as intrauterine growth restriction (IUGR) and often associated with small‐for‐gestational age (SGA), and large‐for‐gestational age (LGA), which may lead to fetal macrosomia; both have been associated with a variety of adverse maternal and perinatal outcomes. Screening for, and adequate management of, fetal growth abnormalities are essential components of antenatal care, and fetal ultrasound plays a key role in assessment of these conditions. The fetal biometric parameters measured most commonly are biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC) and femur diaphysis length (FL). These biometric measurements can be used to estimate fetal weight (EFW) using various different formulae1. It is important to differentiate between the concept of fetal size at a given timepoint and fetal growth, the latter being a dynamic process, the assessment of which requires at least two ultrasound scans separated in time. Maternal history and symptoms, amniotic fluid assessment and Doppler velocimetry can provide additional information that may be used to identify fetuses at risk of adverse pregnancy outcome. Accurate estimation of gestational age is a prerequisite for determining whether fetal size is appropriate‐for‐gestational age (AGA). Except for pregnancies arising from assisted reproductive technology, the date of conception cannot be determined precisely. Clinically, most pregnancies are dated by the last menstrual period, though this may sometimes be uncertain or unreliable. Therefore, dating pregnancies by early ultrasound examination at 8–14 weeks, based on measurement of the fetal crown–rump length (CRL), appears to be the most reliable method to establish gestational age. Once the CRL exceeds 84 mm, HC should be used for pregnancy dating2–4. HC, with or without FL, can be used for estimation of gestational age from the mid‐trimester if a first‐trimester scan is not available and the menstrual history is unreliable. When the expected delivery date has been established by an accurate early scan, subsequent scans should not be used to recalculate the gestational age1. Serial scans can be used to determine if interval growth has been normal. In these Guidelines, we assume that the gestational age is known and has been determined as described above, the pregnancy is singleton and the fetal anatomy is normal. Details of the grades of recommendation used in these Guidelines are given in Appendix 1. Reporting of levels of evidence is not applicable to these Guidelines.

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)0.007
metaresearch head score (Gemma)0.038
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: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.038
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.004
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0030.004
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0220.017

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 head0.023
GPT teacher head0.332
Teacher spread0.308 · 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
GenreMethods

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

Citations698
Published2019
Admission routes1
Has abstractyes

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