Clinical Outcomes and Management of NAAT-Positive/Toxin-Negative <i>Clostridioides difficile</i> Infection: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Standalone nucleic acid amplification tests (NAATs) are frequently used to diagnose Clostridioides difficile infections (CDI), although they may be unable to distinguish colonization from disease. A 2-stage algorithm pairing NAATs with toxin immunoassays (Toxin) may improve specificity. We evaluated clinical outcomes of patients who were NAAT+/Toxin+ versus NAAT+/Toxin- and treated versus untreated NAAT+/Toxin- cases through systematic review and meta-analysis. METHODS: We searched EMBASE and MEDLINE from inception to April 1, 2023 for articles comparing CDI outcomes among symptomatic patients tested by NAAT and Toxin tests. The risk differences (RD) of all-cause mortality and CDI recurrence were computed by random effects meta-analysis between patients who were NAAT+/Toxin+ and NAAT+/Toxin-, as well as between patients who were NAAT+/Toxin- and treated or untreated. RESULTS: Twenty-six observational studies comprising 12 737 patients were included. The 30-day all-cause mortality was not significantly different between those who were NAAT+/Toxin+ (8.4%) and NAAT+/Toxin- (6.7%) (RD = 0.41%, 95% confidence interval [CI] = -.67, 1.49). Recurrence at 60 days was significantly higher among patients who were NAAT+/Toxin+ (19.8%) versus NAAT+/Toxin- (11.0%) (RD = 7.65%, 95% CI = 4.60, 10.71). Among treated compared to untreated NAAT+/Toxin- cases, the all-cause 30-day mortalities were 5.0% and 12.7%, respectively (RD = -7.45%, 95% CI = -12.29, -2.60), but 60-day recurrence was not significantly different (11.6% vs 7.0%, respectively; RD = 5.25%, 95% CI -1.71, 12.22). CONCLUSIONS: Treatment of patients who were NAAT+/Toxin- was associated with reduced all-cause mortality but not recurrence. Although subject to the inherent limitations of observational studies, these results suggest that some patients who are NAAT+/Toxin- may benefit from treatment.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".