Discordance between testosterone measurement methods in castrated prostate cancer patients
Bibliographic record
Abstract
Failure to suppress testosterone below 0.7 nM in castrated prostate cancer patients is associated with poor clinical outcomes. Testosterone levels in castrated patients are therefore routinely measured. Although mass spectrometry is the gold standard used to measure testosterone, most hospitals use an immunoassay method. In this study, we sought to evaluate the accuracy of an immunoassay method to measure castrate testosterone levels, with mass spectrometry as the reference standard. We retrospectively evaluated a cohort of 435 serum samples retrieved from castrated prostate cancer patients from April to September 2017. No follow-up of clinical outcomes was performed. Serum testosterone levels were measured in the same sample using liquid chromatography coupled with tandem mass spectrometry and electrochemiluminescent immunoassay methods. The mean testosterone levels were significantly higher with immunoassay than with mass spectrometry (0.672 ± 0.359 vs 0.461 ± 0.541 nM; P < 0.0001). Half of the samples with testosterone ≥0.7 nM assessed by immunoassay were measured <0.7 nM using mass spectrometry. However, we observed that only 2.95% of the samples with testosterone <0.7 nM measured by immunoassay were quantified ≥0.7 nM using mass spectrometry. The percentage of serum samples experiencing testosterone breakthrough at >0.7 nM was significantly higher with immunoassay (22.1%) than with mass spectrometry (13.1%; P < 0.0001). Quantitative measurement of serum testosterone levels >0.7 nM by immunoassay can result in an inaccurately identified castration status. Suboptimal testosterone levels in castrated patients should be confirmed by either mass spectrometry or an immunoassay method validated at low testosterone levels and interpreted with caution before any changes are made to treatment management.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".