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Record W2048691299 · doi:10.1155/2013/953150

Preeclampsia Is a Biomarker for Vascular Disease in Both Mother and Child: The Need for a Medical Alert System

2013· article· en· W2048691299 on OpenAlexaff
Julie Hakim, Mary K. Senterman, Antoine M. Hakim

Bibliographic record

VenueInternational Journal of Pediatrics · 2013
Typearticle
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsOntario Stroke NetworkOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicinePreeclampsiaDiseaseBiomarkerVulnerability (computing)FetusFetal programmingProinflammatory cytokinePregnancyEclampsiaBlood pressureObstetricsInternal medicineInflammation

Abstract

fetched live from OpenAlex

This paper reviews the literature pertaining to the impact of preeclampsia not only on the mother but particularly on the children. The review points to the higher blood pressure in children born to preeclamptic mothers compared to controls, their increased tendency to suffer strokes, the reduction in their cognitive ability, and their vulnerability to depression. Mechanisms that may induce these changes are emphasized, particularly the placental vascular insufficiency and the resulting hypoxic and proinflammatory environments in which the fetus develops. The hypothesis proposed is that these changes in the fetal-placental environment result in epigenetic programming of the child towards a higher propensity for vascular disease. The review's main recommendation is that, within ethical boundaries, the medical records of individuals born to preeclamptic mothers should clearly indicate this event and should be made available to the affected individuals so that preventive measures against vascular complications and lifestyle changes that may mitigate the latter can be instituted.

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)0.005
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.002
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 head0.013
GPT teacher head0.276
Teacher spread0.263 · 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 designTheoretical or conceptual
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

Citations75
Published2013
Admission routes1
Has abstractyes

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