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Record W2104059030 · doi:10.1016/j.jmhg.2005.07.001

The good, the bad, and the unknown of late onset hypogonadism: the urological perspective

2005· article· en· W2104059030 on OpenAlexaff
F. Jockenhövel, Joel M. Kaufman, G. Mickisch, Álvaro Morales, Christina Wang

Bibliographic record

VenueThe Journal of Men s Health and Gender · 2005
Typearticle
Languageen
FieldMedicine
TopicHormonal and reproductive studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsPerspective (graphical)MedicinePsychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The evaluation and treatment of late-onset hypogonadism (LOH) is one of the manifold tasks of the urologist in daily practice. Diagnosis of LOH is based on an adequate history and physical examination as well as biochemical assessment of androgen deficiency. Treatment of hypogonadism with androgen therapy has been shown to be highly effective in re-establishing normal testosterone levels and maintaining libido, sexual function and improving muscle mass and bone mineral density. Recent pharmacological research has developed short-acting testosterone gel preparations and a 3-month depot injection. The possible development or unmasking of prostate cancer has long been a major concern in treating LOH. Consequently underlying prostate disease should be excluded by diagnostic measures before androgen therapy is initiated. Evidence suggests that provided hypogonadal men are carefully counselled and closely monitored during treatment testosterone therapy can be initiated after radical prostatectomy for non-metastatic prostate cancer with undetectable prostate specific antigen of long duration.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.007
Scholarly communication0.0040.006
Open science0.0010.002
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0040.001

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 head0.032
GPT teacher head0.320
Teacher spread0.288 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2005
Admission routes1
Has abstractyes

Explore more

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