Best practices in mitigating the risk of biotin interference with laboratory testing
Bibliographic record
Abstract
Dietary biotin intake does not typically result in blood biotin concentrations that exceed interference thresholds for in vitro diagnostic tests. However, recent trends of high-dose biotin supplements and clinical trials of very high biotin doses for patients with multiple sclerosis have increased concerns about biotin interference with immunoassays. Estimates of the prevalence of high biotin intake vary, and patients may be unaware that they are taking biotin. Since 2016, 92 cases of suspected biotin interference have been reported to the US Food and Drug Administration. Immunoassays at greatest risk from biotin interference include thyroid and reproductive hormones, cardiac, and immunosuppressive drug tests. Several case studies have highlighted the challenge of biotin interference with thyroid hormone assays and the potential misdiagnosis of Graves' disease. Biotin interference should be suspected when immunoassay test results are inconsistent with clinical information; a clinically relevant biotin interference happens when the blood biotin concentration is high and the assay is sensitive to biotin. We propose a best practice workflow for laboratory scientists to evaluate discrepant immunoassay results, comprising: (1) serial dilution; (2) retesting after biotin clearance and/or repeat testing on an alternate platform; and (3) confirmation of the presence of biotin using depletion protocols or direct measurement of biotin concentrations. Efforts to increase awareness and avoid patient misdiagnosis should focus on improving guidance from manufacturers and educating patients, healthcare professionals, and laboratory staff. Best practice guidance for laboratory staff and healthcare professionals would also provide much-needed information on the prevention, detection, and management of biotin interference.
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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 | 0.097 | 0.206 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".