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Record W4309118999 · doi:10.3390/jcm11226759

Contribution of Second Trimester Sonographic Placental Morphology to Uterine Artery Doppler in the Prediction of Placenta-Mediated Pregnancy Complications

2022· article· en· W4309118999 on OpenAlexaff
Eran Ashwal‏, Jasmine Ali-Gami, Amir Aviram, Stefania Ronzoni, Elad Mei‐Dan, John‏ Kingdom, Nir Melamed

Bibliographic record

VenueJournal of Clinical Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsMount Sinai HospitalHealth Sciences CentreNorth York General HospitalSunnybrook Health Science CentreUniversity of TorontoMcMaster University
Fundersnot available
KeywordsMedicineUterine arteryObstetricsPlacentaPreeclampsiaPregnancyGestationIntrauterine growth restrictionUterusFetusGynecologyInternal medicineBiology

Abstract

fetched live from OpenAlex

Background: Second-trimester uterine artery Doppler is a well-established tool for the prediction of preeclampsia and fetal growth restriction. At delivery, placentas from affected pregnancies may have gross pathologic findings. Some of these features are detectable by ultrasound, but the relative importance of placental morphologic assessment and uterine artery Doppler in mid-pregnancy is presently unclear. Objective: To characterize the association of second-trimester sonographic placental morphology markers with placenta-mediated complications and determine whether these markers are predictive of placental dysfunction independent of uterine artery Doppler. Methods: This was a retrospective cohort study of patients with a singleton pregnancy at high risk of placental complications who underwent a sonographic placental study at mid-gestation (160/7−246/7 weeks’ gestation) in a single tertiary referral center between 2016−2019. The sonographic placental study included assessment of placental dimensions (length, width, and thickness), placental texture appearance, umbilical cord anatomy, and uterine artery Doppler (mean pulsatility index and early diastolic notching). Placental area and volume were calculated based on placental length, width, and thickness. Continuous placental markers were converted to multiples on medians (MoM). The primary outcome was a composite of early-onset preeclampsia and birthweight < 3rd centile. Results: A total of 429 eligible patients were identified during the study period, of whom 45 (10.5%) experienced the primary outcome. The rate of the primary outcome increased progressively with decreasing placental length, width, and area, and increased progressively with increasing mean uterine artery pulsatility index (PI). By contrast, placental thickness followed a U-shaped relationship with the primary outcome. Placental length, width, and area, mean uterine artery PI and bilateral uterine artery notching were all associated with the primary outcome. However, in the adjusted analysis, the association persisted only for placenta area (adjusted odds ratio [aOR] 0.21, 95%-confidence interval [CI] 0.06−0.73) and mean uterine artery PI (aOR 11.71, 95%-CI 3.84−35.72). The area under the ROC curve was highest for mean uterine artery PI (0.80, 95%-CI 0.71−0.89) and was significantly higher than that of placental area (0.67, 95%-CI 0.57−0.76, p = 0.44). A model that included both mean uterine artery PI and placental area did not significantly increase the area under the curve (0.82, 95%-CI 0.74−0.90, p = 0.255), and was associated with a relatively minor increase in specificity for the primary outcome compared with mean uterine artery PI alone (63% [95%-CI 58−68%] vs. 52% [95%-CI 47−57%]). Conclusion: Placental area is independently associated with the risk of placenta-mediated complications yet, when combined with uterine artery Doppler, did not further improve the prediction of such complications compared with uterine artery Doppler alone.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.073
GPT teacher head0.382
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 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".

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Citations9
Published2022
Admission routes1
Has abstractyes

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