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Record W4386629938 · doi:10.1002/uog.16708

P22.01: Preliminary investigation of the utility of <scp>MRI</scp> for measuring the hematocrit in fetal anemia

2016· article· en· W4386629938 on OpenAlexaff
Anqi Duan, Johannes Keunen, Sharon Portnoy, Meng Yuan Zhu, C. Anastasiadis, Greg Ryan, Prashob Porayette, Brahmdeep S. Saini, John G. Sled, Christopher K. Macgowan, Mike Seed

Bibliographic record

VenueUltrasound in Obstetrics and Gynecology · 2016
Typearticle
Languageen
FieldMedicine
TopicBlood groups and transfusion
Canadian institutionsMount Sinai HospitalHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineHematocritFetusGestational ageAnemiaUmbilical veinNuclear medicineSurgeryPregnancyObstetricsInternal medicine

Abstract

fetched live from OpenAlex

The detection of fetal anemia by middle cerebral artery peak systolic velocity is less reliable with advancing gestational age (GA) and prior transfusion. We sought to investigate the feasibility and accuracy of a new MRI method for measuring fetal hematocrit (Hct) in vivo in anemic fetuses. We recruited 4 pregnant women suspected of carrying anemic fetuses between 19 and 38 weeks GA. Two fetuses had Rh alloimmunisation, one had alpha-thalassemia and two had twin anemia-polycythemia sequence. The fetuses underwent MRI scans either immediately before or after their intrauterine transfusions (IUT), or both. The scans were performed on a 1.5T Siemens scanner and included T1 and T2 mapping of the intrahepatic umbilical vein. We used vessel T1 and T2 to calculate Hct according to our previously published technique [Portnoy et al. ISMRM 2015]. MRI Hct was compared with the Hct from cordocentesis samples obtained during IUT. The Hct values we calculated from T1 and T2 times showed excellent agreement with the gold-standard laboratory Hct (see figure 1). The mean difference between the MRI-predicted Hct and the laboratory values is 3.7%. Our method was accurate for a wide range of fetal hematocrit, from anemia to polycythemia, and for both before and after IUT. Supporting information can be found in the online version of this abstract Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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)0.001
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.023
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 head0.019
GPT teacher head0.234
Teacher spread0.215 · 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.

Study designObservational
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
Published2016
Admission routes1
Has abstractyes

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