An Assessment of the Terminology Used by Diplomates and Students to Describe the Character of Equine Mitral and Aortic Valve Regurgitant Murmurs: Correlations with the Physical Properties of the Sounds
Bibliographic record
Abstract
Twenty students and 16 diplomates listened to 7 recordings made from 7 horses with either aortic (n = 3) or mitral valve (n = 4) regurgitant murmurs. A total of 30 different terms were used to describe the character of these murmurs. However, only 4 terms were used in a repeatable and consistent manner. Most people described the character of a given mitral or aortic valve murmur with 1 or 2 terms. Diplomates drew from a pool of terms that was about half the size of that used by students–.1 ±2.0 terms for diplomats (mean ±1 SD) versus 13.1 ±1.8 terms for students (P < .001). Only blowing, honking, buzzing, and musical were markedly associated with the recording played. Frequency analysis of the murmurs allowed them to be classified as containing harmonics (n = 4) or not containing harmonics (n = 3). Blowing was used to describe murmurs without harmonics on 39 of 48 occasions and corresponds to the term noisy used in some older descriptions of equine murmurs. Honking, musical, and buzzing were markedly associated with murmurs that contained harmonics; these terms were used 23, 13, and 12 of a possible 64 times, respectively. The frequency of buzzing and honking murmurs (72.7 ±9.3 and 88.4 ±46.3 Hz, respectively) was markedly lower than that of musical murmurs (156.8 ±81.1 Hz) (all P values <.01). Honking murmurs (0.392 ±0.092 seconds) were shorter than those described as buzzing or musical (0.496 ±0.205 and 0.504 ±0.116 seconds, respectively). The data suggest that the terminology for the character of aortic and mitral regurgitant murmurs should be restricted to 4 terms: blowing, honking, buzzing, and musical. Honking, buzzing, and musical describe murmurs with a peak dominant frequency and harmonics; blowing describes murmurs without a peak frequency. Effective communication could be enhanced by playing examples of reference sounds when these terms are taught so that nomenclature is used more uniformly. Key words: Cardiac; Heart; Learning; Meaning.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".