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Record W4296718179 · doi:10.2196/37452

The Fetal Growth Restriction at Term Managed by Angiogenic Factors Versus Feto-Maternal Doppler (GRAFD) Trial to Avoid Adverse Perinatal Outcomes: Protocol for a Multicenter, Open-Label, Randomized Controlled Trial

2022· article· en· W4296718179 on OpenAlexvenueno aff
Pablo García‐Manau, Manel Mendoza, Erika Bonacina, Raquel Martin-Alonso, Lourdes Martín, Ana Palacios, Maria Luisa Sanchez, Cristina Lesmes Heredia, Ivan Hurtado, Esther Pérez Carbajo, A. Tubau, Patricia Ibañez, Marina Alcoz, N. Valiño, Elena Moreno, Carlota Borrero, Esperanza Garcı́a, Eva López‐Quesada, Sonia Diaz, José Román Broullón, Mireia Teixidor, Carolina Chulilla, M. M. Gil, Monica López, Amparo Candela-Hidalgo, Andrea Salinas-Amoros, Anna Camille Moreno, Francesca Morra, Óscar Vaquerizo, Beatriz Soriano, Marta Fabre, Elena Gomez-Valencia, Ana Cuiña, Nicolas Alayon, José Antonio Sainz‐Bueno, Àngels Vives, E Estève, Vanesa Ocaña, Miguel Ángel López, Anna Maroto, E. Carreras

Bibliographic record

VenueJMIR Research Protocols · 2022
Typearticle
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsnot available
Fundersnot available
KeywordsRandomized controlled trialMedicineProtocol (science)Adverse effectObstetricsFetal growthPregnancyFetusPediatricsInternal medicineAlternative medicineBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Fetal smallness affects 10% of pregnancies. Small fetuses are at a higher risk of adverse outcomes. Their management using estimated fetal weight and feto-maternal Doppler has a high sensitivity for adverse outcomes; however, more than 60% of fetuses are electively delivered at 37 to 38 weeks. On the other hand, classification using angiogenic factors seems to have a lower false-positive rate. Here, we present a protocol for the Fetal Growth Restriction at Term Managed by Angiogenic Factors Versus Feto-Maternal Doppler (GRAFD) trial, which compares the use of angiogenic factors and Doppler to manage small fetuses at term. OBJECTIVE: The primary objective is to demonstrate that classification based on angiogenic factors is not inferior to estimated fetal weight and Doppler at detecting fetuses at risk of adverse perinatal outcomes. METHODS: This is a multicenter, open-label, randomized controlled trial conducted in 20 hospitals across Spain. A total of 1030 singleton pregnancies with an estimated fetal weight ≤10th percentile at 36+0 to 37+6 weeks+days will be recruited and randomly allocated to either the control or the intervention group. In the control group, standard Doppler-based management will be used. In the intervention group, cases with a soluble fms-like tyrosine kinase to placental growth factor ratio ≥38 will be classified as having fetal growth restriction; otherwise, they will be classified as being small for gestational age. In both arms, the fetal growth restriction group will be delivered at ≥37 weeks and the small for gestational age group at ≥40 weeks. We will assess differences between the groups by calculating the relative risk, the absolute difference between incidences, and their 95% CIs. RESULTS: Recruitment for this study started on September 28, 2020. The study results are expected to be published in peer-reviewed journals and disseminated at international conferences in early 2023. CONCLUSIONS: The angiogenic factor-based protocol may reduce the number of pregnancies classified as having fetal growth restriction without worsening perinatal outcomes. Moreover, reducing the number of unnecessary labor inductions would reduce costs and the risks derived from possible iatrogenic complications. Additionally, fewer inductions would lower the rate of early-term neonates, thus improving neonatal outcomes and potentially reducing long-term infant morbidities. TRIAL REGISTRATION: ClinicalTrials.gov NCT04502823; https://clinicaltrials.gov/ct2/show/NCT04502823. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/37452.

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)0.017
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.021
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.020
Meta-epidemiology (narrow)0.0050.002
Meta-epidemiology (broad)0.0100.004
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0030.003
Open science0.0030.001
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0210.003

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 head0.203
GPT teacher head0.512
Teacher spread0.309 · 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 designRandomized trial
Domainnot available
GenreProtocol

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

Citations6
Published2022
Admission routes1
Has abstractyes

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