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Record W2411300299 · doi:10.1373/clinchem.2016.256198

Prostate-Specific Antigen as a Marker of Hyperandrogenism in Women and Its Implications for Antidoping

2016· review· en· W2411300299 on OpenAlexaff
Natasha Musrap, Eleftherios P. Diamandis

Bibliographic record

VenueClinical Chemistry · 2016
Typereview
Languageen
FieldMedicine
TopicHormonal and reproductive studies
Canadian institutionsUniversity of TorontoUniversity Health NetworkMount Sinai Hospital
Fundersnot available
KeywordsHyperandrogenismhirsutismPolycystic ovaryMedicineProstate-specific antigenBiomarkerProstate cancerTestosterone (patch)HormoneOncologyProstateCancerAntigenInternal medicineGynecologyAndrogenBreast cancerEndocrinologySex hormone-binding globulinImmunologyBiologyDiabetes mellitus

Abstract

fetched live from OpenAlex

BACKGROUND: Since its discovery in the 1970s, prostate-specific antigen (PSA) has become widely known as a biomarker of prostate cancer in males but has often been overlooked in female malignancies. Although the serum concentration of PSA differs between men and women by about 1000-fold, studies have suggested that PSA concentrations drastically differ among healthy females and those who exhibit increased androgen production. CONTENT: There have been reports of increased PSA expression in women exhibiting hyperandrogenic states, including polycystic ovary syndrome and hirsutism, as well as marked increases in a subset of breast cancer patients. These findings have not only revealed the remarkable diagnostic potential of PSA in a diverse range of clinical conditions but also point to its potential of becoming a useful biomarker of steroid hormone doping among female athletes. Recently, highly sensitive assays that can measure PSA at low limits of detection have been developed, which will aid in the discrimination of PSA between these different conditions. SUMMARY: The overall aim of this review is to revisit the expression of PSA in hormonally-regulated tissues and in female malignancies, and to demonstrate how the regulation of PSA permits its use in antidoping initiatives.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.125
GPT teacher head0.438
Teacher spread0.313 · 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 designNot applicable
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

Citations13
Published2016
Admission routes1
Has abstractyes

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