An Approach for Evaluating Potential Screening Thresholds Using Biomarker Population Distribution and Analytical Imprecision
Bibliographic record
Abstract
BACKGROUND: A common approach in laboratory medicine is to use a simple but sensitive test to screen samples to identify those that require additional investigation with a more complex and informative method. Selection of screening thresholds can be guided by biomarker distribution in the tested population and the analytical imprecision of the method. METHODS: A simulation using joint probabilities derived from the population distribution for galactose-1-phosphate uridylyltransferase (GALT) activity and the analytical imprecision for the GALT assay was used to estimate the number of samples that would require repeat analysis and the number of samples with possibly false-negative screening determinations due to analytical imprecision. RESULTS: In the case of GALT activity, screening a conservative initial threshold 6 standard deviations from the confirmation threshold can essentially eliminate the chance of a false-negative screening determination due to analytical imprecision. The trade-off is a greater number of samples requiring follow-up testing (n = 222, equivalent to 0.15% of samples annually). CONCLUSIONS: Selection of thresholds in a screening algorithm is informed by estimates of the number of samples that would require repeat testing and the number that could be false negative due to analytical imprecision.
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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 | 0.041 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".