Falsely Elevated Steroid Hormones in a Postmenopausal Woman Due to Laboratory Interference
Bibliographic record
Abstract
Laboratory interference is a drawback in hormonal testing, and clinicians should have a high index of suspicion when faced with biochemical results discordant with the patient's clinical manifestations. A 62-year-old postmenopausal woman initially consulted her primary care physician for mood lability; laboratory workup showed markedly elevated levels of total serum estradiol, progesterone, testosterone, and cortisol as measured by immunoassay. Further investigation demonstrated no evidence of estrogen effect on uterus, no adrenal or adnexal mass, and no evidence of Cushing syndrome. Conventional techniques to unmask laboratory interference, such as dilution, antigen precipitation, and using a different immunoassay did not unveil a potential laboratory interference. The patient had no apparent risk factor for analytic interference, such as absent rheumatoid factor and heterophilic antibodies, but had only mild monoclonal IgG hypergammaglobulinemia. In this case, mass spectrometry unmasked the false elevation in steroid hormones. Interference of gammaglobulins or antibodies with the labeling and separation process of the assay could be the culprits. In conclusion, we report a unique case of multiple steroid hormones elevations due to laboratory interference unmasked by mass spectrometry.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".