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Record W4303199206 · doi:10.2147/ijgm.s364189

An Individualized Approach to Managing Testosterone Therapy in the Primary Care Setting

2022· review· en· W4303199206 on OpenAlexaboutno aff
Adrian S. Dobs, Kevin James Campbell

Bibliographic record

VenueInternational Journal of General Medicine · 2022
Typereview
Languageen
FieldMedicine
TopicHormonal and reproductive studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePrimary careTestosterone (patch)Intensive care medicineBioinformaticsFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

The incidence of testosterone deficiency and the use of testosterone therapy have increased in recent years, and currently the majority of testosterone prescriptions in the United States and Canada are written by primary care physicians. Meanwhile, the range of available testosterone therapy formulations has widened to include buccal tablets, intramuscular injections, transdermal gels, intranasal gel, subcutaneous injections, oral capsules, and subdermal pellets, each with unique pharmacokinetic and clinical characteristics. Despite the growing use of testosterone therapy and its overall efficacy and safety as demonstrated in clinical trials, concerns exist about the potential impact of testosterone therapy on spermatogenesis and fertility, development of prostate cancer, and risk of polycythemia and cardiovascular events. In addition, ongoing research aims to better characterize the effects of testosterone therapy in specific populations, such as patients aged 65 years and older, patients with obesity and type 2 diabetes, and transgender patients. The range of treatment options and the diversity of patients' goals, preferences, comorbidities, and risk factors necessitate an individualized approach to testosterone therapy that considers each patient's clinical needs alongside the distinct features of different testosterone formulations.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score0.667

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.088
GPT teacher head0.391
Teacher spread0.303 · 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 designOther design
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

Citations6
Published2022
Admission routes1
Has abstractyes

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