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Record W4405010300 · doi:10.52673/18570461.24.3-74.04

Evoluția sarcinii la pacienta cu Rhesus negativ

2024· article· en· W4405010300 on OpenAlexaboutno aff
Hristiana Caproș, Apostolos Athanasiadis

Bibliographic record

VenueAkademos · 2024
Typearticle
Languageen
FieldMedicine
TopicBlood groups and transfusion
Canadian institutionsnot available
Fundersnot available
KeywordsCourse (navigation)PregnancyObstetricsPsychologyMedicineBiologyEngineeringGenetics

Abstract

fetched live from OpenAlex

Feto-maternal isoimmunization is a complex phenomenon in which the maternal immune system produces antibodies directed against antigens present on fetal blood cells. A common example of feto-maternal immunization is Rh isoimmunization, which occurs when an Rh-negative mother is exposed to the blood of an Rh-positive fetus. Maternal-fetal Rh isoimmunization causes more than 160,000 perinatal deaths and 100,000 cases of disability each year. In order to reduce perinatal mortality and morbidity from this pathology, recent guidelines from the French Society of Obstetrics and Gynecology (CNGOF, 2017), the International Federation of Gynecology and Obstetrics (FIGO, 2021), and the Canadian Society of Obstetrics and Gynecology (SOGC, 2024) have revised the management of pregnancy in Rh negative patients. In line with these recommendations, blood type and Rh determination is part of the recommended early pregnancy screening, ideally at the first antenatal visit. In non-immunized Rh-negative pregnant women with an Rh-positive partner, a non-invasive prenatal test can be recommended: Rh determination based on fetal acellular deoxyribonucleic acid present in maternal blood. Routine administration of anti-RhD immunoglobulin prenatally (at 2834 weeks amenorrhea (SA) and postpartum (within 72 hours after delivery) to unimmunized patients with Rh-positive infants allows reduction of feto-maternal sensitization.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.284
Teacher spread0.272 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations0
Published2024
Admission routes1
Has abstractyes

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