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Record W4234030686 · doi:10.24124/2019/58939

Primary care use of testosterone therapy to benefit women experiencing distress related to decreased sexual desire after surgical menopause

2019· dissertation· en· W4234030686 on OpenAlexaffabout
Damian Rawnsley

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicSexual function and dysfunction studies
Canadian institutionsOkanagan CollegeOkanagan University CollegeUniversity of British Columbia
Fundersnot available
KeywordsTestosterone (patch)DistressSexual desireMenopausePrimary careMedicineOophorectomyHormone therapyPsychologyGynecologyClinical psychologyPsychotherapistFamily medicineHuman sexualityInternal medicineHysterectomySurgeryCancerGender studies

Abstract

fetched live from OpenAlex

Testosterone is a biologically significant hormone hypothesized to play a role in supporting women’s sexual desire. Women that undergo bilateral oophorectomy experience a marked change in hormonal status including a precipitous decline in testosterone levels. A number of these women experience a corresponding loss of sexual desire which can provoke distress and motivate them to seek sexual health care. Clinical research and guidelines suggest that testosterone therapy may be beneficial in improving sexual desire in these women. However, in Canada there are no licensed testosterone products for women. Consequently, clinicians are required to individually determine how to provide exogenous testosterone therapy. The purpose of this integrative literature review is to provide evidence-informed recommendations, derived from current literature, to inform nurse practitioners practicing in primary care settings how to safely prescribe, monitor and evaluate testosterone therapy. Research and education recommendations are also presented.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.029
GPT teacher head0.292
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 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
Published2019
Admission routes2
Has abstractyes

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