Bibliographic record
Abstract
This is the 2nd edition of a truly unique book. It is the only book that I am aware of that lists disease predispositions under breed, not vice versa. It is fairly easy in clinical practice to research specific disease conditions and find a list of breeds prone to that condition. It is virtually impossible to research a breed and find all disease predispositions that might affect them, until now… Indirectly, this book highlights the risks associated with selective breeding, as too often we see these predispositions increase with inbreeding and selection for extreme phenotypes that are considered desirable. It is intended to be used by veterinarians, breeders, and pet owners, although by the nature of the material, the authors acknowledge that some of the material may be too technical for the layperson. A “Breed Predisposition” is defined here as “an increased risk for a condition in a breed, which may or not be an inherited disease”. While attempting to gather such information for each breed, the authors also point out that there are limitations with current methods of recording diagnoses or case findings. Not all data from diseased animals may have universally been documented. On the other hand, they did not want to omit findings for “increased risk” in cases that would likely be denied a place in a peer-reviewed journal article. The result has been an astoundingly wide range of references; at least one for every condition for every breed listed. They include peer- and non-peer-reviewed journals, conference proceedings, and some classic textbooks. In this 2nd edition, the authors have tried to be stricter with which conditions are included as breed predispositions. They have succeeded in their attempt to include information on modes of inheritance, sex and age predispositions, how common the condition is in the breed population, and the risk of developing the disease in the general population. The organization of this book is straightforward and easy to use. Most (90%) of the text covers canine conditions, although the feline sections appear well-documented too. Listings are alphabetical by breed and then further by condition or disease. As mentioned earlier, no finding is given a place in this book without a reference of some form. At the back of the book there is a small but useful list for genetic test providers worldwide. “Breed Predispositions to Diseases in Cats and Dogs” is a remarkable achievement. It has been well-researched and made as complete as is possible. It deserves a place on every veterinarian’s shelf.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".