Feline coronavirus and feline infectious peritonitis (FIP) – Russian roulette for your pet
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Feline coronavirus and feline infectious peritonitis (FIP) – Russian roulette for your pet Utilising Machine Learning on clinical datasets could help to crack the enigma of feline infectious peritonitis diagnosis. Coronaviruses came to the forefront of public consciousness in 2019 with the outbreak of the SARS-CoV-2 pandemic. However, this family of viruses has long been recognised as important pathogens of animals and man. Feline coronavirus (FCoV) is a ubiquitous pathogen of cats, which can sometimes cause a devastating disease called ‘feline infectious peritonitis’ (FIP) in both domestic and wild felids. This virus is common among pet cats and in multi-cat households and shelters, where its prevalence can be extremely high. Infection is reasonably innocuous for most cats, who may experience asymptomatic infection or develop a mild gastrointestinal upset. However, similar to COVID-19 in humans, sometimes infection has more severe consequences. In a small fraction of cases, usually between 5 and 10% of FCoV-infected individuals, (1) cats develop a severe aberrant immune response to the virus, resulting in FIP. Different types of FIP occur, affecting different tissues, and until very recently, the disease was invariably fatal.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it