Evaluation of relationships between results of electrocardiography and echocardiography in 341 chimpanzees (Pan troglodytes)
Bibliographic record
Abstract
Abstract OBJECTIVE To examine potential relationships between ECG characteristics and echocardiographic measures of cardiac structure in chimpanzees ( Pan troglodytes ). ANIMALS 341 chimpanzees (175 males and 166 females) from 5 sanctuaries and 2 zoological collections. PROCEDURES Chimpanzees were anesthetized for routine health examinations between May 2011 and July 2017 as part of the International Primate Heart Project and, during the same anesthetic events, underwent 12-lead ECG and transthoracic echocardiographic assessments. Relationships between results for ECG and those for echocardiographic measures of atrial areas, left ventricular internal diameter in diastole (LVIDd), and mean left ventricular wall thicknesses (MLVWT) were assessed with correlational analysis, then multiple linear regression analyses were used to create hierarchical models to predict cardiac structure from ECG findings. RESULTS Findings indicated correlations ( r = −0.231 to 0.310) between results for ECG variables and echocardiographic measures. The duration and amplitude of P waves in lead II had the strongest correlations with atrial areas. The Sokolow-Lyon criteria, QRS-complex duration, and R-wave amplitude in leads V 6 and II had the strongest correlations with MLVWT, whereas the Sokolow-Lyon criteria, QRS-complex duration, and S-wave amplitude in leads V 2 and V 1 had the strongest correlations with LVIDd. However, the ECG predictive models that were generated only accounted for 17%, 7%, 11%, and 8% of the variance in the right atrial end-systolic area, left atrial end-systolic area, MLVWT, and LVIDd, respectively. CONCLUSIONS AND CLINICAL RELEVANCE Results indicated that relationships existed between ECG findings and cardiac morphology in the chimpanzees of the present study; however, further research is required to examine whether the predictive models generated can be modified to improve their clinical utility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".