Maximizing confidence in a negative result: Quantitative sample adequacy control
Bibliographic record
Abstract
Quantitative PCR (qPCR) is a leading screening tool, permitting rapid detection of pathogens and the maintenance of effective infection control programs. Unfortunately, qPCR assays frequently do not incorporate Sample Adequacy Control (SAC). A SAC controls for the quantity, quality and adequacy of the specimen. Without SAC, the confidence in a negative result remains questionable and the efficacy of screening is compromised. Ultimately, the exclusion of SAC from qPCR may result in false negative results. One should consider SAC to be an integral critical type of laboratory control; addressing diverse analytical problems, such as sample adequacy, sample processing and assay inhibition. Following distribution of cycle threshold values (Cq) of Influenza A positive results and Cq values of SAC, obtained from nasopharyngeal swabs, we showed that the confidence in a negative result cannot be guaranteed in the presence of a weak positive SAC signal (late Cq values). Herein, we explain why widespread inclusion of sample adequacy control in routine screening is blocked. A protocol and methods for SAC threshold establishment are offered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".