Comparison of 2 sampling methods for molecular detection of bacteria or fungi from feline hair and scale specimens
Bibliographic record
Abstract
Skin diseases of cats are among the most frequent client motivations for a veterinary consultation. Both carpet and toothbrush sampling are commonly used to obtain hair and scale samples for microbiologic testing. Although molecular tests have become more accessible and more widely used by clinicians, the ideal collection method for clinical specimens is unclear. To assess their performance in retrieving microbial DNA from clinical samples, we compared the bacterial and fungal DNA load in hair and skin scale samples collected using carpet or toothbrush methods. We evaluated sample DNA yield using fluorometry, spectrophotometry, and quantitative PCR. Despite no measurable differences in sample weight, toothbrush samples yielded significantly higher bacterial (p = 0.028) and fungal (p = 0.005) DNA loads compared to carpet samples, regardless of disease status. The toothbrush method was more effective in harvesting microbial DNA from hair and skin scale samples.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".