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Record W2116035936 · doi:10.7861/clinmedicine.13-1-57

Renal disease and hypertension in pregnancy

2013· review· en· W2116035936 on OpenAlexaff
Ines Palma-Reis, Alina Vais, Catherine Nelson‐Piercy, Anita Banerjee

Bibliographic record

VenueClinical Medicine · 2013
Typereview
Languageen
FieldMedicine
TopicPregnancy and Medication Impact
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMedicinePregnancyDiseaseKidney diseaseKidneyIntensive care medicineChronic renal diseaseObstetricsPediatricsInternal medicine

Abstract

fetched live from OpenAlex

Because women are becoming pregnant at a later age, hypertension is more commonly encountered in pregnancy. In addition, with increasing numbers of young women living with renal transplants and kidney disease, it is important for physicians to be aware of the effects of pregnancy on these diseases. A multidisciplinary approach is essential to assess and care for pregnant women with kidney disease. Pre-pregnancy counselling should be offered to all women with chronic kidney disease. A review of medication to avoid teratogenicity and optimise the disease prior to conception is the ideal. Pregnancy may be the first medical review for a young woman, who may present with a previously undiagnosed renal problem.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.005
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.239
GPT teacher head0.478
Teacher spread0.239 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations21
Published2013
Admission routes1
Has abstractno

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