International trial of online auditory training programme for distinguishing innocent and pathological murmurs
Bibliographic record
Abstract
AIM: Recognition of normal and abnormal heart sounds and murmurs is an important but declining clinical skill among practitioners. Current teaching methods are often ineffective. This may result from inadequate repetition and normal-abnormal comparisons needed for auditory recognition. This paper describes a rapid new method of teaching murmur recognition using principles of auditory training. METHODS: Participants were 120 Australian and 42 Canadian medical students. The medical students were randomised to intervention and control (no intervention) groups. The 1-h online programme structured like a computer game used auditory training methodology to teach students to distinguish between innocent and pathological murmurs. Participants underwent pre- and post-testing on 20 paediatric murmurs. Post-testing occurred immediately following training and after 2 months. Twenty-two Canadian medical students were retested 1 year later with a brief mastery-style reinforcement programme. RESULTS: Median pre- and post-test scores improved in about 1 h from 75-95% (P < 0.001) for Australian students and 85-95% (P = 0.004) for Canadian students. Two-month post-test scores declined for Australian students to 85% (P = 0.001), and for Canadian students to 85% (P = 0.02). Australian controls had no significant change during the study period, whereas Canadian controls improved slightly. The group receiving reinforcement after 1 year had a median final score of 90%. CONCLUSIONS: This auditory training programme rapidly teaches students to distinguish innocent and pathological murmurs with at least 90% accuracy. The skill declines within 2 months but can be restored with brief mastery reinforcement 1 year later.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".