The quantitative ADAM questionnaire: a new tool in quantifying the severity of hypogonadism
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".