Increased environmental sample area and recovery of<i>Clostridium difficile</i>spores from hospital surfaces by quantitative PCR and enrichment culture
Bibliographic record
Abstract
OBJECTIVE: Clostridium difficile spores play an important role in transmission and can survive in the environment for several months. Optimal methods for measuring environmental C. difficile are unknown. We sought to determine whether increased sample surface area improved detection of C. difficile from environmental samples. SETTING: Samples were collected from 12 patient rooms in a tertiary-care hospital in Toronto, Canada. METHODS: Samples represented small surface-area and large surface-area floor and bedrail pairs from single-bed rooms of patients with low (without prior antibiotics), medium (with prior antibiotics), and high (C. difficile infected) shedding risk. Presence of C. difficile in samples was measured using quantitative polymerase chain reaction (qPCR) with targets on the 16S rRNA and toxin B genes and using enrichment culture. RESULTS: Of the 48 samples, 64·6% were positive by 16S qPCR (geometric mean, 13·8 spores); 39·6% were positive by toxin B qPCR (geometric mean, 1·9 spores); and 43·8% were positive by enrichment culture. By 16S qPCR, each 10-fold increase in sample surface area yielded 6·6 times (95% CI, 3·2-13) more spores. Floor surfaces yielded 27 times (95% CI, 4·9-181) more spores than bedrails, and rooms of C. difficile-positive patients yielded 11 times (95% CI, 0·55-164) more spores than those of patients without prior antibiotics. Toxin B qPCR and enrichment culture returned analogous findings. CONCLUSIONS: Clostridium difficile spores were identified in most floor and bedrail samples, and increased surface area improved detection. Future research aiming to understand the role of environmental C. difficile in transmission should prefer samples with large surface areas.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".