Evaluation of PetrifilmsTM as a diagnostic test to detect bovine mastitis organisms in Kenya
Bibliographic record
Abstract
The study purpose was to validate Petrifilms(TM) (3M Microbiology, 2005) against standard culture methods in the diagnosis of bovine mastitis organisms in Kenya. On 128 smallholder dairy cattle farms in Kenya, between June 21, 2010 and August 31, 2010, milk samples from 269 cows that were positive on California Mastitis Test (CMT) were cultured using standard laboratory culture methods and Petrifilms(TM) (Aerobic Count and Coliform Count -3M Microbiology, 2005), and results were compared. Staphylococcus aureus was the most common bacterium isolated (73 % of samples). Clinical mastitis was found in only three cows, and there were only two Gram-negative isolates, making it impossible to examine the agreement between the two tests for Gram-negative- or clinical mastitis samples. The observed agreement between the standard culture and Petrifilm(TM) (3M Microbiology, 2005) results for Gram-positive isolates was 85 %, and there was fair agreement beyond that expected due to chance alone, with a kappa (κ) of 0.38. Using culture results as a gold standard, the Petrifilms(TM) had a sensitivity of 90 % for Gram-positive samples and specificity of 51 %. With 87 % of CMT-positive samples resulting in Gram-positive pathogens cultured, there was a positive predictive value of 93 % and a negative predictive value of 43 %. Petrifilms(TM) should be considered for culture of mastitis organisms in developing countries, especially when Gram-positive bacteria are expected.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".