Association of cancer-related mortality, age and gonadectomy in golden retriever dogs at a veterinary academic center (1989-2016)
Bibliographic record
Abstract
Golden retriever dogs have been reported to have an increased prevalence of cancer compared to other breeds. There is also controversy over the effect spay or neuter status might have on longevity and the risk for developing cancer. The electronic medical records system at an academic center was searched for all dogs who had a necropsy exam from 1989-2016. 9,677 canine necropsy examinations were completed of which 655 were golden retrievers. Age was known for 652 with a median age of death 9.15 years. 424 of the 652 (65.0%) were determined to have died because of cancer. The median age for dying of a cause other than cancer was 6.93 years while those dying of cancer had a median age of 9.83 years (p<0.0001). There was no significant difference in the proportion of intact males and castrated males dying of cancer (p = 0.43) but a greater proportion of spayed females died of cancer compared to intact females (p = 0.001). Intact female dogs had shorter life spans than spayed female dogs (p<0.0001), but there were no differences between intact and castrated males. Intriguingly, being spayed or neutered did not affect the risk of a cancer related death but increasing age did. The most common histologic diagnosis found in golden retrievers dying of cancer was hemangiosarcoma (22.64%) followed by lymphoid neoplasia (18.40%). Overall golden retriever dogs have a substantial risk of cancer related mortality in a referral population and age appears to have a larger effect on cancer related mortality than reproductive status.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".