Replicate PCR Testing and Probit Analysis for Detection and Quantitation of <i>Chlamydia pneumoniae</i> in Clinical Specimens
Bibliographic record
Abstract
Nucleic acid amplification of clinical specimens with low target concentration has variable sensitivity. We examined whether testing multiple aliquots of extracted DNA increased the sensitivity and reproducibility of Chlamydia pneumoniae detection by PCR. Nested and non-nested C. pneumoniae PCR assays were compared using 10 replicates of 16 serial dilutions of C. pneumoniae ATCC VR-1310. The proportion positive versus the C. pneumoniae concentration was modeled by probit regression analysis. To validate the model, 10 replicates of 26 previously positive patient specimens of peripheral blood mononuclear cells (PBMC), sputum, or nasopharyngeal swabs (NPS) were tested. The proportion of replicates that were positive varied with the concentration of C. pneumoniae in the sample. At concentrations above 5 infection-forming units (IFU)/ml, both nested and non-nested PCR assay sensitivities were 90% or greater. The nested PCR was more sensitive (median detection, 0.35 versus 0.61 IFU/ml; relative median detection, 0.58; 95% confidence interval, 0.31 to 0.99; P = 0.04). In clinical specimens, replicate PCR detected 15 of 26 (nested) versus 1 of 26 (non-nested, P < 0.001). For PBMC specimens, testing 1, 3, or 5 replicates detected 3, 5, or 9 of 10 positive specimens, respectively. Median C. pneumoniae concentrations were estimated at 0.07 IFU/ml for PBMC and at <0.03 IFU/ml for NPS specimens. We conclude that performing 5 or 10 replicates considerably increased the sensitivity and reproducibility of C. pneumoniae PCR and enabled quantitation for clinical specimens. Due to sampling variability, PCR tests done without replication may miss a large proportion of positive specimens, particularly for specimens with small amounts of target C. pneumoniae DNA present.
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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 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".