Bibliographic record
Abstract
Heartworm disease caused by infection with Dirofilaria immitis causes a pathological impact on dogs and an economic impact on pet owners. This chapter outlines the worldwide economic impact of heartworm disease on the pet owner. The cost is calculated by separating cost components into the cost of prevention (heartworm medication), the cost of treatment, and the opportunity cost of treatment. From a geographic standpoint, separate estimates on the impact within the key heartworm countries (United States, Australia, Japan, Italy, Spain, and Canada) and the rest of the world in aggregate are provided. These calculations provide an estimated total global cost of heartworm disease of US$ 2.47 billion dollars. The cost of prevention makes up 93%, or $2.30 billion of this total, with the cost of treatment representing 6%, or $146 million, and the opportunity cost to the pet owner of 1% or $24.6 million. From a geographic standpoint, the United States accounts for 65% of the costs ($1.6 billion), Japan 10% ($257 million), Canada 7% ($174 million), Italy 6% ($150 million), Spain 4% ($101 million), Australia 3% ($80 million), and the rest of the world 4% ($106 million). The global costs of heartworm will likely increase in the future as the disease spreads geographically, the prevalence of heartworm increases in countries where it is currently found, and the standard of care for pets continues to increase throughout the world.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".