Determination of Real‐Time Polymerase Chain Reaction Uncertainty of Measurement Using Replicate Analysis and a Graphical User Interface with Fieller’s Theorem
Bibliographic record
Abstract
BACKGROUND: Disease monitoring of viruses using real-time polymerase chain reaction (PCR) requires knowledge of the precision of the test to determine what constitutes a significant change. Calculation of quantitative PCR confidence limits requires bivariate statistical methods. OBJECTIVE: To develop a simple-to-use graphical user interface to determine the uncertainty of measurement (UOM) of BK virus, cytomegalovirus (CMV) and Epstein-Barr virus (EBV) real-time PCR assays. METHODS: Thirty positive clinical samples for each of the three viral assays were repeated once. A graphical user interface was developed using a spreadsheet (Excel, Microsoft Corporation, USA) to enable data entry and calculation of the UOM (according to Fieller's theorem) and PCR efficiency. RESULTS: The confidence limits for the BK virus, CMV and EBV tests were ∼0.5 log, 0.5 log to 1.0 log, and 0.5 log to 1.0 log, respectively. The efficiencies of these assays, in the same order were 105%, 119% and 90%. The confidence limits remained stable over the linear range of all three tests. DISCUSSION: A >5 fold (0.7 log) and a >3-fold (0.5 log) change in viral load were significant for CMV and EBV when the results were ≤1000 copies/mL and >1000 copies/mL, respectively. A >3-fold (0.5 log) change in viral load was significant for BK virus over its entire linear range. PCR efficiency was ideal for BK virus and EBV but not CMV. Standardized international reference materials and shared reporting of UOM among laboratories are required for the development of treatment guidelines for BK virus, CMV and EBV in the context of changes in viral load.
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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 | 0.039 | 0.113 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".