Do you hear what you see? Utilizing phonocardiography to enhance proficiency in cardiac auscultation
Bibliographic record
Abstract
Introduction Cardiac auscultation skills have proven difficult to train and maintain. The authors investigated whether using phonocardiograms as visual adjuncts to audio cases improved first-year medical students’ cardiac auscultation performance. Methods The authors randomized 135 first-year medical students using an email referral link in 2018 and 2019 to train using audio-only cases (audio group) or audio with phonocardiogram tracings (combined group). Training included 7 cases with normal and abnormal auscultation findings. The assessment included feature identification and diagnostic accuracy using 14 audio-only cases, 7 presented during training, and 7 alternate versions of the same diagnoses. The assessment—administered immediately after training and repeated 7 days later—prompted participants to identify the key features and diagnoses for 14 audio-only cases. Key feature scores and diagnostic accuracy were compared between groups using repeated measures ANOVA. Results Mean key feature scores were statistically significantly higher in the combined group (70%, 95% CI 67–75%) compared to the audio group (61%, 95% CI 56–66%) (F(1,116) = 6.144, p = 0.015, d s = 0.45). Similarly, mean diagnostic accuracy in the combined group (68%, 95% CI 62–73%) was significantly higher than the audio group, although with small effect size (59%, 95% CI 54–65%) (F(1,116) = 4.548, p = 0.035, d s = 0.40). Time on task for the assessment and prior auscultation experience did not significantly impact performance on either measure. Discussion The addition of phonocardiograms to supplement cardiac auscultation training improves diagnostic accuracy and heart sound feature identification amongst novice students compared to training with audio alone.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".