Long-term incidence and risk of noncardiovascular and all-cause mortality in apparently healthy cats and cats with preclinical hypertrophic cardiomyopathy
Bibliographic record
Abstract
BACKGROUND: Epidemiologic knowledge regarding noncardiovascular and all-cause mortality in apparently healthy cats (AH) and cats with preclinical hypertrophic cardiomyopathy (pHCM) is limited, hindering development of evidence-based healthcare guidelines. OBJECTIVES: To characterize/compare incidence rates, risk, and survival associated with noncardiovascular and all-cause mortality in AH and pHCM cats. ANIMALS: A total of 1730 client-owned cats (722 AH, 1008 pHCM) from 21 countries. METHODS: Retrospective, multicenter, longitudinal, cohort study. Long-term health data were extracted by medical record review and owner/referring veterinarian interviews. RESULTS: Noncardiovascular death occurred in 534 (30.9%) of 1730 cats observed up to 15.2 years. Proportion of noncardiovascular death did not differ significantly between cats that at study enrollment were AH or had pHCM (P = .48). Cancer, chronic kidney disease, and conditions characterized by chronic weight-loss-vomiting-diarrhea-anorexia were the most frequently recorded noncardiovascular causes of death. Incidence rates/risk of noncardiac death increased with age in AH and pHCM. All-cause death proportions were greater in pHCM than AH (65% versus 40%, respectively; P < .001) because of higher cardiovascular mortality in pHCM cats. Comparing AH with pHCM, median survival (study entry to noncardiovascular death) did not differ (AH, 9.8 years; pHCM, 8.6 years; P = .10), but all-cause survival was significantly shorter in pHCM (P = .0001). CONCLUSIONS AND CLINICAL IMPORTANCE: All-cause mortality was significantly greater in pHCM cats due to disease burden contributed by increased cardiovascular death superimposed upon noncardiovascular death.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".