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Record W2922564474 · doi:10.1097/jxx.0000000000000186

Best practices in care for menopausal patients: 16 years after the Women's Health Initiative

2019· article· en· W2922564474 on OpenAlexaff
Terri DeNeui, Judith A. Berg, A. G. Howson

Bibliographic record

VenueJournal of the American Association of Nurse Practitioners · 2019
Typearticle
Languageen
FieldMedicine
TopicMenopause: Health Impacts and Treatments
Canadian institutionsEmergent BioSolutions (Canada)
Fundersnot available
KeywordsMedicineMenopauseClinical trialRandomized controlled trialHormone therapyPostmenopausal womenHealth careIntensive care medicineMEDLINEGynecologyFamily medicinePhysical therapyBreast cancerInternal medicineCancer

Abstract

fetched live from OpenAlex

The Women's Health Initiative (WHI) was a large, randomized clinical trial funded by the National Institutes of Health to determine whether menopause hormone therapy (MHT) prevented heart disease, breast and colorectal cancer, and osteoporotic fractures in postmenopausal women. Two WHI trials were stopped early, and the findings had a profound effect on the clinical practice guidelines related to postmenopausal health. This article provides an overview of the WHI MHT clinical trials and findings, discusses the early stoppage of the trials and subsequent implications, and details the current nomenclature and treatment options for women transitioning through menopause in light of the WHI. This study is based on a comprehensive literature review and an education activity developed by the American Association of Nurse Practitioners. To best serve patients and individualize therapy, clinicians must provide the best estimate of potential risks or benefits to the individual patient. It is important to balance evidence of symptom relief with long-term risks and benefits that fit the patient's characteristics of family and personal health history. Armed with evidence to support various hormonal and non-hormonal options, well-informed clinicians can counsel women about MHT and potentially avoid negative impact on quality of life.

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)0.002
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 score0.021
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.365
Teacher spread0.341 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Association of Nurse PractitionersSame topicMenopause: Health Impacts and TreatmentsFrench-language works237,207