Comparison of serum testosterone and estradiol measurements in 3174 European men using platform immunoassay and mass spectrometry; relevance for the diagnostics in aging men
Bibliographic record
Abstract
BACKGROUND: The limitations of serum testosterone and estradiol (E(2)) measurements using non-extraction platform immunoassays (IAs) are widely recognized. Switching to more specific mass spectrometry (MS)-based methods has been advocated, but directly comparative data on the two methods are scarce. METHODS: We compared serum testosterone and E(2) measurements in a large sample of middle-aged/elderly men using a common platform IA and a gas chromatography (GC)-MS method, in order to assess their limitations and advantages, and to diagnose male hypogonadism. Of subjects from the European Male Aging Study (n=3174; age 40-79 years), peripheral serum testosterone and E(2) were analyzed using established commercial platform IAs (Roche Diagnostics E170) and in-house GC-MS methods. RESULTS: Over a broad concentration range, serum testosterone concentration measured by IA and MS showed high correlation (R=0.93, P<0.001), which was less robust in the hypogonadal range (<11 nmol/l; R=0.72, P<0.001). The IA/MS correlation was weaker in E(2) measurements (R=0.32, P<0.001, at E(2) <40.8 pmol/l, and R=0.74, P<0.001, at E(2) >40.8 pmol/l). Using MS as the comparator method, IA ascertained low testosterone compatible with hypogonadism (<11 nmol/l), with 75% sensitivity and 96.3% specificity. The same parameters with IA for the detection of low E(2) (<40.7 pmol/l) were 13.3 and 99.3%, and for high E(2) (>120 pmol/l) 88.4 and 88.6%. CONCLUSION: A validated platform IA is sufficient to detect subnormal testosterone concentrations in the diagnosis of male hypogonadism. The IA used for E(2) measurements showed poor correlation with MS and may only be suitable for the detection of high E(2) in men.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".