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Record W2767436573 · doi:10.28984/drhj.v1i0.4

Achieving Consensus in the Management of Intrahepatic Cholestasis in Pregnancy

2017· article· en· W2767436573 on OpenAlexaffvenueabout
Julianna Briglio, Roberta Heale, Crestina L. Beites, Emily Donato

Bibliographic record

VenueDiversity of Research in Health Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicPregnancy and Medication Impact
Canadian institutionsLaurentian University
Fundersnot available
KeywordsCholestasis of pregnancyContext (archaeology)MedicinePregnancyGuidelineDelphi methodFetal distressCholestasisIntensive care medicineEnvironmental healthObstetricsFetusPathologyGeographyInternal medicine

Abstract

fetched live from OpenAlex

The incidence of intrahepatic cholestasis (ICP) in pregnancy, a liver condition, is increasing, suggesting a greater awareness of the disease. While this condition is an annoyance for a mother, typically causing intense itchiness of the hands and soles of the feet, she will return to normal after delivery. However, this condition has the potential to cause premature delivery, fetal distress and fetal death, so appropriate management is very important. Currently, guidelines available in North America conflict with guidelines in the United Kingdom. The purpose of this research is to establish evidence-based, current practice guidelines for managing cholestasis in pregnancy within a local context. A modified Delphi technique will be utilized with the goal of achieving consensus among local obstetricians about the management of ICP. It is anticipated that two rounds of questionnaires will be disseminated to the group. The content of the questionnaires and the resulting guideline, will take into consideration the unique health care environment and resources in one northeastern Ontario community.

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)0.007
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.262
GPT teacher head0.487
Teacher spread0.225 · 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

Citations0
Published2017
Admission routes3
Has abstractyes

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