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Record W1969762553 · doi:10.1038/ijir.2009.35

The quantitative ADAM questionnaire: a new tool in quantifying the severity of hypogonadism

2009· article· en· W1969762553 on OpenAlexaff
Osama G. Mohamed, Robert E. Freundlich, Hassan K. Dakik, Ethan D. Grober, Bobby B. Najari, Larry I. Lipshultz, Mohit Khera

Bibliographic record

VenueInternational Journal of Impotence Research · 2009
Typearticle
Languageen
FieldMedicine
TopicHormonal and reproductive studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineAndrogen deficiencyAndrogenProstatectomyProstate cancerTestosterone (patch)Shim (computing)GynecologyPopulationErectile dysfunctionInternal medicineUrologyEndocrinologyHormoneCancer

Abstract

fetched live from OpenAlex

Androgen deficiency is a pervasive problem in the older male population and is thought to be responsible for many symptoms once considered to be the result of normal aging. Numerous methods have been proposed to facilitate the detection of men at risk for androgen deficiency. In this article, we propose a novel screening tool, the quantitative Androgen Deficiency in the Aging Male (qADAM) questionnaire and report its successful use in quantifying the severity of androgen deficiency in a group of older men. Fifty-seven males scheduled to undergo radical prostatectomy for prostate cancer completed the qADAM as well as the Sexual Health Inventory for Men (SHIM) and the Expanded Prostate Cancer Index Composite hormonal/sexual (EPICh/EPICs) questionnaires. Thirty-four men also had serum testosterone levels measured for comparison. The qADAM showed statistically significant correlation to the SHIM (P=0.001), EPICh (P=0.016), EPICs (P= <0.001), and serum testosterone (P=0.046). The qADAM represents a viable alternative to existing questionnaires used to detect androgen deficiency and to assess response to treatment.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.873
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
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 integrity0.0000.001
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 head0.160
GPT teacher head0.481
Teacher spread0.321 · 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

Citations99
Published2009
Admission routes1
Has abstractyes

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