Evaluation of the Petrifilm™ Culture System for the Identification of Mastitis Bacteria as Compared to Standard Bacteriological Methods
Bibliographic record
Abstract
The largest cost associated with treating clinical mastitis during lactation is the discarded milk during and after therapy. To justify this cost, one should ensure that the present infection will respond to antibiotic therapy. Most intramammary antibiotics target gram-positive cocci organisms, and are not particularly effective against Mycoplasma spp, yeasts and mild infections caused by gram-negative pathogens. Similarly, research has demonstrated that other organisms, such as Streptococcus uberis, are highly responsive to intramammary antibiotics, or that the cure rates achieved by various intramammary treatments are not significantly better than no treatment. Thus, there has been an increasing demand for research towards the development of on-farm mastitis culturing media for early identification and accurate classification of bacterial species. Petrifilm™ plates are selective culture media products, which are used for rapid bacteriological isolation and enumeration from food products. The 3M Petrifilm™ products are small, playing card size, sample-ready plates that will allow users to easily and efficiently perform on-site microbial identification. Petriflim™ plates that are potentially useful for mastitis diagnoses are the Aerobic Count Plates, Coliform Count Plates, and Staph Express Count Plates, due to their ease of use, and the ability to reach a diagnosis in as little as 24 hours. The usefulness of the Petrifilm™ culture system for the diagnosis of mastitis has been previously reported.1 The objective of the current project was to determine the test characteristics of Petrifilm™ to characterize the causative organism in producer-defined cases of clinical mastitis.
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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 | 0.003 | 0.003 |
| 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 | 0.000 |
| 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 | 0.001 |
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".