Cardiac Auscultation in the Modern Era
Bibliographic record
Abstract
Competent cardiac auscultation remains a most important skill for the detection of heart disease. Currently it is poorly taught and often ignored or poorly performed, resulting in inaccurate and inefficient patient assessments. This review documents that teaching can be over 90% effective with new, proven teaching methods emphasizing repetition and normal-abnormal comparisons of sounds, using computer-aided and online resources. At present, these concepts are not widely adopted by medical schools. Our current knowledge of teaching heart auscultation is critically reviewed, including traditional bedside, clinic and classroom settings, as well as computer, simulator, and multimedia-based learning. The assessment of auscultation skill in the learning process. The adoption of competence-based learning promises to integrate the assessment of auscultation skill in the learning process. Newer teaching methods, such as auditory training and repetitive listening, offer excellent murmur recognition and diagnosis learning, and hand-held ultrasound is proposed as a helpful adjunct to teaching auscultation. Although ongoing research remains important to develop better teaching methods, the adoption of proven existing concepts has great potential to improve teaching and practice of this valuable skill.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".