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Record W2173796272 · doi:10.5539/gjhs.v8n7p172

Preterm Birth Prevention: Effects of Vaginal Progesterone Administration on Blood Flow Impedance in Uterine-Fetal Circulation by Doppler Sonography

2015· article· en· W2173796272 on OpenAlexvenueno aff
Homeira Vafaei, Tarlan Zamanpour, Hadi Raeisi Shahraki

Bibliographic record

VenueGlobal Journal of Health Science · 2015
Typearticle
Languageen
FieldMedicine
TopicPreterm Birth and Chorioamnionitis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBlood flowFetal circulationFetusObstetricsDoppler sonographyPregnancyGynecologyPlacentaInternal medicineBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: The present study aimed to evaluate the effect of vaginal progesterone administration on maternal and fetal circulation to prevent preterm birth. METHODS: The present prospective study was conducted on 35 women with singleton pregnancy at 18-33 weeks of gestation, who presented with at least one episode of preterm labor or asymptomatic short cervix, or past medical history of preterm birth. Doppler flow and Pulsatility Index (PI) assessment of the umbilical artery, fetal middle cerebral artery, uterine arteries, and ductusvenosus were performed before and 72 h after vaginal progesterone administration. RESULTS: Results showed a significant reduction in the PI of the uterine artery following progesterone administration. Nevertheless, no significant changes were observed in the PI of other vessels. No significant difference was found in Doppler flow parameters in any of the examined vessels before or after progesterone treatment in women with Preterm Labor Pain (PLP). Yet, a statistically significant association was observed between short cervix complication in the current pregnancy and medical history of PLP in the previous pregnancy. CONCLUSION: Treatment with vaginal progesterone reduced the PI in the uterine arteries in the second and third trimesters of pregnancy. Thus, this medication may have useful vasodilatory effects on uterine-fetal vessels.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.019
GPT teacher head0.336
Teacher spread0.317 · 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 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

Citations7
Published2015
Admission routes1
Has abstractyes

Explore more

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