Feline leukemia and feline immunodeficiency virus in Canada - A comment.
Bibliographic record
Abstract
Dear Editor, We would first like to thank The Canadian Veterinary Journal (CVJ) for the August 2011 comprehensive review article entitled “Feline leukemia and feline immunodeficiency virus in Canada: Recommendations for testing and management” (Can Vet J 2011;52:849–855) by Dr. Susan Little et al, which brings much-needed attention to these significant and serious feline infectious diseases. With regards to feline immunodeficiency virus (FIV), while there is some debate as to FIV’s impact on mortality, there is none with regards to its significant negative impact on morbidity, which directly correlates to a patient’s quality of life (1–2). Prevention is important. As such, we were disappointed to read the author’s statement “the use of this [FIV] vaccine cannot be recommended” when reviewing the section on preventative retroviral strategies and this seems based in part, on the findings of a single negative UK-based study that is in stark contrast to multiple other studies demonstrating robust vaccine efficacy. It is our opinion that this failure to provide appropriate efficacy perspective undermines a critical preventative strategy for a disease with such high prevalence and severity. As the vaccine manufacturer, we are quite proud of the fruit borne from the 15 years of collaboration between industry and academia to bring to market the current Boehringer Ingelheim FIV vaccine Fel-O-Vax FIV. During the development of this vaccine, over 60 different challenges have been done assessing vaccine efficacy to Clades A, B, and A/B (most common in Canada) with preventative fractions ranging from 80% to 100% (3–7). The authors do briefly reference the 4 published studies done with Fel-O-Vax FIV without commenting on their efficacy: Kusuhara, 2005 — clade B 2 year natural exposure challenge — 100% (PF) (8); Pu, 2005 — clade B challenge — 100% (PF) (9); Huang, 2004 — heterologous clade A challenge — 82% (PF) (10); Huang, 2010 — clade B challenge — 71% (PF) (11). Doing an average on all of the data from the 9 published studies confirms a preventable fraction above 80% which is considered the standard mark of a good vaccine and clearly shows that Fel-O-Vax FIV vaccine can induce a broad, effective, prophylactic immunity against the heterologous sub-types commonly seen in North America (12). It should also be noted that the FIV vaccine is licensed at a higher licensing level (an aid in the prevention of infection) than most of the other feline vaccines (aid in the prevention of disease). FIV is very prevalent in Canadian cats and vaccination to prevent FIV infection should still be considered as part of the prevention strategy for this disease as recommended by the AAFP in at risk cats (13,14). In fairness to the article text, the authors do raise concern over the interpretation of FIV testing as contributing to their vaccine use recommendations. Based on the vaccine efficacy data provided above, perhaps this is the more clinically relevant issue and we would encourage the Task Force to separate out these two issues to ensure they are specifically addressed and that veterinarians (after reviewing all of the published efficacy data) are provided with the applicable tools to gain and pass along appropriate informed consent, thereby ensuring that individual cats are offered the protection they need should their lifestyle warrant.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.062 | 0.049 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".