<i>Clostridioides difficile</i> Near-Patient Testing Versus Centralized Testing: A Pragmatic Cluster Randomized Crossover Trial
Bibliographic record
Abstract
BACKGROUND: Management of suspected Clostridioides difficile infection (CDI) in the hospital setting typically results in patient isolation, laboratory testing, infection control, and presumptive treatment. We investigated whether implementation of rapid near-patient testing (NPT) reduced patient isolation time, hospital length of stay (LOS), antibiotic usage, and cost. METHODS: A 2-period pragmatic cluster randomized crossover trial was conducted. Thirty-nine wards were randomized into 2 study arms. The primary outcome measure was effect of NPT on patient isolation time using a mixed-effects generalized linear regression model. Secondary outcomes examined were hospital LOS and antibiotic therapy based on a negative binomial regression model. Natural experiment (NE), intention-to-treat (ITT), and per-protocol (PP) analyses were conducted. RESULTS: During the entire study period, a total of 656 patients received NPT for CDI and 1667 received standard-of-care testing. For the primary outcome, a significant decrease of patient isolation time with NPT was observed (NE, 9.4 hours [P < .01]; ITT, 2.3 hours [P < .05]; PP, 6.7 hours [P < .1]). A significant reduction in hospital LOS was observed with NPT for short stay (NE, 47.4% [P < .01]; ITT, 18.4% [P < .01]; PP, 34.2% [P < .01]). Each additional hour delay for a negative result increased metronidazole use (24 defined daily doses per 1000 patients; P < .05) and non-CDI-treating antibiotics by 70.13 mg (P < .01). NPT was found to save 25.48 US dollars per patient when including test cost to the laboratory and patient isolation in the hospital. CONCLUSIONS: This pragmatic cluster randomized crossover trial demonstrated that implementation of CDI NPT can contribute to significant reductions in isolation time, hospital LOS, antibiotic usage, and healthcare cost. Clinical Trials Registration. NCT03857464.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".