Correlation between Clostridium difficile Bacterial Load, Commercial Real-Time PCR Cycle Thresholds, and Results of Diagnostic Tests Based on Enzyme Immunoassay and Cell Culture Cytotoxicity Assay
Bibliographic record
Abstract
The impact of Clostridium difficile fecal loads on diagnostic test results is poorly understood, but it may have clinical importance. In this study, we investigated the relationship between C. difficile fecal load and the results of four assays: a glutamate dehydrogenase (GDH) enzyme immunoassay (EIA), a toxin A/B antigen EIA (ToxAB), a cell culture cytotoxicity assay (CCA), and PCR targeting the tcdB gene. We also compared the PCR cycle threshold (CT) with the results of quantitative culture using Spearman's rank correlation coefficient. Finally, we sequenced the genomes of 24 strains with different detection profiles. A total of 203 clinical samples harboring toxigenic C. difficile were analyzed and sorted into one of four groups: 17 PCR(+) (group 1), 37 PCR(+) GDH(+) (group 2), 24 PCR(+) GDH(+) CCA(+) (group 3), and 125 PCR(+) GDH(+) ToxAB(+) (group 4). The overall median fecal load in log10 CFU/g was 6.67 (interquartile range [IQR], 5.57 to 7.54). The median fecal bacterial load of groups 1, 2, 3, and 4 were 4.15 (IQR, 3.00 to 4.98), 5.74 (IQR, 4.75 to 6.16), 6.20 (IQR, 5.23 to 6.80), and 7.08 (IQR, 6.35 to 7.83), respectively. Group 1 samples had lower fecal loads than those from each of the other groups (P < 0.001). Group 2 samples had lower fecal loads than those from groups 3 and 4 (P < 0.001). There was a significant correlation between PCR CT and fecal loads (ρ = -0.697; P < 0.001). NAP1 strains were associated with the detection of toxins by EIA or CCA (P = 0.041). This study demonstrates an association between C. difficile fecal load and the results of routinely used diagnostic tests.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".