Insurance data for research in companion animals: benefits and limitations
Bibliographic record
Abstract
The primary aim of this article is to review the use of animal health insurance data in the scientific literature, especially in regard to morbidity or mortality in companion animals and horses. Methods and results were compared among studies on similar health conditions from different nations and years. A further objective was to critically evaluate benefits and limitations of such databases, to suggest ways to maximize their utility and to discuss the future use of animal insurance data for research purposes. Examples of studies on morbidity, mortality and survival estimates in dogs and horses, as well as neoplasia in dogs, are discussed.We conclude that insurance data can and should be used for research purposes in companion animals and horses. Insurance data have been successfully used, e.g. to quantify certain features that may have been hitherto assumed, but unmeasured. Validation of insurance databases is necessary if they are to be used in research. This must include the description of the insured population and an evaluation of the extent to which it represents the source population. Data content and accuracy must be determined over time, including the accuracy/consistency of diagnostic information. Readers must be cautioned as to limitations of the databases and, as always, critically appraise findings and synthesize information with other research. Similar findings from different study designs provide stronger evidence than a sole report. Insurance data can highlight common, expensive and severe conditions that may not be evident from teaching hospital case loads but may be significant burdens on the health of a population.
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 | 0.253 | 0.601 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.021 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".