Doppler echocardiography–derived evidence of pulmonary arterial hypertension in dogs with degenerative mitral valve disease: 86 cases (2001–2005)
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of Doppler echocardiography-derived evidence of pulmonary arterial hypertension (DEE-PAH) in dogs with mitral valve disease (MVD) classified according to the International Small Animal Cardiac Health Council (ISACHC) heart failure classification scheme and various echocardiographic and Doppler indices of MVD severity. DESIGN: Retrospective case series. ANIMALS: 617 dogs examined from 2001 to 2005 with MVD in ISACHC classes I to III. PROCEDURES: Dogs were examined echocardiographically. Criteria used for systolic and diastolic DEE-PAH were detection of high tricuspid (> or = 2.5 m/s) and telediastolic pulmonic (> or = 2.0 m/s) valvular peak regurgitant jet velocities, respectively, by use of continuous-wave Doppler echocardiography. RESULTS: 86 (13.9%) dogs with MVD had a diagnosis of DEE-PAH. Severity and prevalence of DEE-PAH increased with ISACHC class (3.0%, 16.9%, 26.7%, and 72.2% prevalences for ISACHC classes Ia, Ib, II, and III, respectively). A significant correlation between systolic or diastolic pulmonary arterial pressure and left atrial-to-aortic diameter ratio (LA/Ao) was detected. Doppler echocardiography-derived evidence of pulmonary arterial hypertension was detected in 18 dogs with values of LA/Ao within reference range, all of which had moderate (n = 2 dogs) or severe (16) mitral valve regurgitation on color Doppler imaging. CONCLUSIONS AND CLINICAL RELEVANCE: The prevalence and degree of DEE-PAH were related to the severity of MVD. Changes associated with DEEPAH may be detected in early stages of the disease, but only in dogs with severe mitral valve regurgitation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".