Diagnosis and Evaluation of Hypertrophic Cardiomyopathy
Bibliographic record
Abstract
Hypertrophic cardiomyopathy (HCM) is a relatively common often inherited global heart disease, with complex phenotypic and genetic expression and natural history, affecting both genders and many races and cultures. Prevalence is 1:200-1:500, largely based on the disease phenotype with imaging, inferring that 750,000 Americans may be affected by HCM. However, cross-sectional data show that only a fraction are clinically diagnosed, suggesting under-recognition, with most clinicians exposed to small segments of the broad disease spectrum. Highly effective HCM management strategies have emerged, altering clinical course and substantially lowering mortality and morbidity rates. These advances underscore the importance of reliable HCM diagnosis with echocardiography and cardiac magnetic resonance. Family screening with noninvasive imaging will identify relatives with the HCM phenotype, while genetic analysis recognizes preclinical sarcomere gene carriers without left ventricular hypertrophy, but with the potential to transmit disease. Comprehensive initial patient evaluations are important for reliable diagnosis, accurate portrayal of HCM and family history, risk stratification, and distinguishing obstructive versus nonobstructive forms.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".