2020 AAFP Feline Retrovirus Testing and Management Guidelines
Bibliographic record
Abstract
CLINICAL IMPORTANCE: Feline leukemia virus (FeLV) and feline immunodeficiency virus (FIV) infections are found in cats worldwide. Both infections are associated with a variety of clinical signs and can impact quality of life and longevity. SCOPE: This document is an update of the 2008 American Association of Feline Practitioners' feline retrovirus management guidelines and represents current knowledge on pathogenesis, diagnosis, prevention and treatment of retrovirus infections in cats. TESTING AND INTERPRETATION: Although vaccines are available for FeLV in many countries and for FIV in some countries, identification of infected cats remains an important factor for preventing new infections. The retrovirus status of every cat at risk of infection should be known. Cats should be tested as soon as possible after they are acquired, following exposure to an infected cat or a cat of unknown infection status, prior to vaccination against FeLV or FIV, and whenever clinical illness occurs. It might not be possible to determine a cat's infection status based on testing at a single point in time; repeat testing using different methods could be required. Although FeLV and FIV infections can be associated with clinical disease, some infected cats, especially those infected with FIV, can live for many years with good quality of life. MANAGEMENT OF INFECTED CATS: There is a paucity of data evaluating treatments for infected cats, especially antiretroviral and immunomodulatory drugs. Management of infected cats is focused on effective preventive healthcare strategies, and prompt identification and treatment of illness, as well as limiting the spread of infection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 0.026 |
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".