MétaCan
Menu
Back to cohort
Record W4386630167 · doi:10.1002/uog.16443

OP29.07: Non‐invasive <i>in utero</i> measurements of placental oxygen transport using <scp>MRI</scp>

2016· article· en· W4386630167 on OpenAlexaff
Brahmdeep S. Saini, Meng Yuan Zhu, Sharon Portnoy, Prashob Porayette, Jessie Mei Lim, Abing Duan, John G. Sled, Rachel M. Wald, Rory Windrim, Christopher K. Macgowan, John‏ Kingdom, Mike Seed

Bibliographic record

VenueUltrasound in Obstetrics and Gynecology · 2016
Typearticle
Languageen
FieldMedicine
TopicFetal and Pediatric Neurological Disorders
Canadian institutionsMount Sinai HospitalToronto General HospitalHospital for Sick Children
Fundersnot available
KeywordsFetusMedicinePlacentaOxygen transportHematocritIn uteroOxygenVenous bloodUterusNuclear medicineInternal medicinePregnancyChemistryBiology

Abstract

fetched live from OpenAlex

To investigate the use of MRI to assess placental function in terms of placental-fetal oxygen (O2) transport in utero. Eight women with normal pregnancies were scanned using a 1.5 T Siemens MRI system. To measure placental-fetal O2 transport, blood flow (Q) and oxygen content (C) were measured in blood vessels supplying (uterine arteries and fetal descending aorta) and draining (uterine, ovarian and umbilical veins) the placenta. Phase contrast MRI was used to measure Q. Vascular blood T1 and T2 MRI relaxometry were used to measure oxygen saturation and hematocrit to derive C. The oxygen delivery (Do2) and return (Ro2) were calculated as C*Q. Oxygen consumption (Vo2) was calculated using the arterio-venous difference in C of blood supplied to and drained from an organ and multiplying it by Q. All oxygen transport measures were indexed to fetal weight, which was calculated from the 3D MRI acquisition of the uterus. The mean placental-Vo2 was 4.1 ml/min/kg, which accounted for 33.9% of the total placental-fetal-Vo2. The mean fetal-Vo2 was 8.0 ml/min/kg and the mean placental-to fetal-Vo2 ratio was 0.53 (see table 1).

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)0.011
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 score0.032
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.026
GPT teacher head0.248
Teacher spread0.221 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueUltrasound in Obstetrics and GynecologySame topicFetal and Pediatric Neurological DisordersFrench-language works237,207