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Pregnancy in End Stage Renal Disease

2011· article· en· W1715452451 on OpenAlexaff
Michelle Hladunewich, Adam Engel Hercz, Johannes Keunen, Christopher T. Chan, Andreas Pierratos

Bibliographic record

VenueSeminars in Dialysis · 2011
Typearticle
Languageen
FieldMedicine
TopicPregnancy and Medication Impact
Canadian institutionsHumber River Regional HospitalUniversity Health NetworkUniversity of TorontoHealth Sciences CentreMount Sinai HospitalSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicinePregnancyIntensive care medicineHemodialysisObstetricsNephrologyInfertilityDialysisEnd stage renal diseasePeritoneal dialysisPreeclampsiaRenal replacement therapyDiseaseGynecologyInternal medicine

Abstract

fetched live from OpenAlex

The ovulatory menstrual cycle is known to be affected on multiple levels in women with advanced renal disease. Menstrual irregularities, sexual dysfunction, and infertility worsen in parallel with the renal disease. Pregnancy in women with ESRD on dialysis is therefore uncommon. Furthermore, when pregnancy does occur, it can prove hazardous to both mother and baby owing to a multitude of potential complications including accelerated hypertension and preeclampsia, poor fetal growth, anemia, and polyhydramnios. Data are emerging, however, to suggest that pregnancy while on intensified renal replacement regimens may result in better pregnancy outcomes, and emerging trends include the decreased rate of therapeutic abortions probably reflecting a change in counseling practices over time. Nevertheless, a pregnant woman on intensive dialysis requires meticulous follow-up by a dedicated team including nephrology, obstetrics, and a full multidisciplinary staff. In this article, we will address fertility issues in young women with ESRD, review pregnancy outcomes in women on both hemodialysis and peritoneal dialysis, and provide suggestions for the management of the pregnant women on intensive hemodialysis.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.031
GPT teacher head0.291
Teacher spread0.260 · 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

Citations62
Published2011
Admission routes1
Has abstractyes

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