Detecting Clostridioides (Clostridium) difficile using canine teams: What does the nose know?
Bibliographic record
Abstract
Background: (CD) in the environment; however, the primary odour of interest on which the dogs alert is unclear. Aim: To evaluate the inter-rater reliability of two canine detection teams for their ability to discriminate between scent pads containing CD-toxin-positive and -negative odours and their ability to discriminate between clostridial strains. Methods: During a six-month period, two canine teams were tested weekly for their ability to detect CD-toxin-positive odours and discriminate between these and -negative odours. To further determine the canines' discrimination capability, scent pads impregnated with odours from reference isolates representing common CD toxin types (including toxin-negative CD isolates) or from clinical isolates representing other clostridial species were used. Results: A total of 264 samples were tested with an overall sensitivity of 94.7% (Team A) and 86.8% (Team B) and specificities of 96.9% and 98.7%, respectively. Inter-rater reliability was very good (Cohen's kappa 0.87). When challenged with toxin- and non-toxin-producing strains, the teams alerted on 96.3% of all CD isolate odours (including nontoxigenic strains) and 46.7% of closely related species. Conclusions: The canine teams exhibited strong inter-rater reliability on both clinical faecal specimens and reference CD isolates (both toxin and non-toxin producing) but were challenged to discriminate between CD and closely related clostridial species. These findings strongly support the development of scent detection programmes provided dogs and their handlers are properly trained and used in the right context.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 | 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".