MétaCan
Menu
Back to cohort
Record W2027957039 · doi:10.3109/14767058.2014.909804

Obstetrical and neonatal outcomes in renal transplant recipients

2014· article· en· W2027957039 on OpenAlexaff
Kholoud Arab, Lisa Oddy, Valérie Patenaude, Haim A. Abenhaim

Bibliographic record

VenueThe Journal of Maternal-Fetal & Neonatal Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicPregnancy and Medication Impact
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsRenal transplantMedicineIntensive care medicineInternal medicinePediatricsTransplantation

Abstract

fetched live from OpenAlex

OBJECTIVE: To measure the incidence and outcomes of pregnancies in renal transplant (RT) patients and to identify risk factors of adverse pregnancy outcomes. METHODS: We conducted a population-based retrospective cohort study using the United States Nationwide Inpatient Sample from 2003-2010. The incidence of pregnancies in women with RT was measured and logistic regression analysis was used to estimate the adjusted effect of RT on maternal and fetal outcomes. RESULTS: We identified 375 deliveries in patients with a RT among 7094300 births for an overall incidence of 5.3 cases per 100000 births over 8 years. Maternal complications, including preeclampsia OR=9.87 (7.76, 12.55) and blood transfusion OR=2.29 (1.69, 3.12) were more common in women with RT as compared to in women without. RT pregnancies were also complicated by an increased risk of preterm birth OR=4.65 (3.72, 5.81), intrauterine fetal death OR=3.67 (1.89, 7.15) and fetal congenital anomalies OR=5.28 (2.81, 9.90). Among women with RT and pre-existing hypertension, the risk of intrauterine growth restriction (IUGR) was considerably increased from 4.3% to 21.8%, OR=3.79 (1.67, 8.62). CONCLUSION: Pregnancies in RT patients are associated with an increased risk of maternal and fetal morbidities. Among women with RT, pre-existing hypertension strongly increases the risk of IUGR.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.278
Teacher spread0.264 · 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

Citations15
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Maternal-Fetal & Neonatal MedicineSame topicPregnancy and Medication ImpactFrench-language works237,207