Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it