High Prevalence of Multistep Algorithms in Diagnostic <i>Clostridioides difficile</i> Laboratory Testing
Bibliographic record
Abstract
CONTEXT.—: Laboratory testing practices for diagnosis of Clostridioides difficile infection (CDI) have evolved in response to published guidelines, availability of highly sensitive nucleic acid amplification tests (NAATs), perceived problems with the specificity of NAATs, and CDI reporting requirements. OBJECTIVE.—: To assess the current state of laboratory practice for diagnostic CDI testing. DESIGN.—: An optional 8-item supplemental questionnaire was distributed in December 2019 to the 1374 laboratories participating in the College of American Pathologists C difficile Detection (CDF) proficiency testing program challenge CDF-C. RESULTS.—: Of 1374 CDF-C participants, 1160 (84.4%) responded, predominantly representing laboratories based in the United States (1077 of 1160; 92.8%). The majority reported using a multistep testing algorithm (684 of 1159; 59.0%). Initial testing with a glutamate dehydrogenase and toxin A/B combination test followed by NAAT for discrepant results was the most common testing method (360 of 1146; 31.4%). NAAT alone (299 of 1146; 26.1%) was next, then NAAT followed by an assay that included toxin A/B enzyme immunoassay if NAAT is positive (258 of 1146; 22.5%). Only 5.4% (62 of 1146) reported using toxin A/B immunoassay alone. Most respondents (1093 of 1131; 96.6%) reported rejecting CDI tests on formed stool, but rejection of CDI testing in pediatric patients was uncommon (211 of 1131; 18.7%). Rejection of CDI testing in patients using laxatives was reported more often by US-based respondents (379 of 1054 [36.0%] versus 9 of 77 [11.7%], P < .001). CONCLUSIONS.—: Multistep algorithms for CDI diagnosis are widely used in line with published recommendations. Most respondents reported rejection of formed stool for CDI testing, but few reported rejection of testing in infants and patients taking laxatives, suggesting these may be areas of opportunity for laboratories to pursue in improving CDI testing practices.
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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 | 0.022 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".