An evidence review and gap analysis for obstructive hypertrophic cardiomyopathy
Bibliographic record
Abstract
BACKGROUND: Patients with obstructive hypertrophic cardiomyopathy (oHCM) have a substantial humanistic, clinical, and economic burden due to the array of symptoms and complications associated with the disease. The objective of this review was to identify key evidence gaps related to oHCM, specifically in Europe, North America, and Japan. METHODS: A targeted literature review was conducted using PubMed to identify English-language studies published between 2012 and 2022 assessing patients with HCM/oHCM in France, Germany, Italy, Spain, the United Kingdom (UK), the United States (US), Canada, and Japan. Outcomes of interest were epidemiology, natural history, pathophysiology, management, and clinical, economic, and humanistic burden. Identified studies were assessed qualitatively to characterize evidence gaps. RESULTS: Among 2,262 abstracts and 531 full-text articles screened, 178 articles were included from PubMed searches. An additional 16 unique studies were identified via a supplemental Google Scholar search initially conducted in January 2023 and updated in July 2024. Disease natural history, pathophysiology, and management were well documented globally. Significant evidence gaps were noted for the epidemiology, treatment, and burden of oHCM. Although multiple US studies were identified on the clinical, economic, and humanistic burden of oHCM, and one clinical burden study was found for Japan, there was a lack of evidence for France, Germany, Italy, Spain, the UK, and Canada. CONCLUSIONS: Major evidentiary gaps exist for the epidemiology, treatment, and burden of oHCM. Future research should address these gaps, with a specific focus on generating real-world evidence for Canada and European countries that will support the evaluation of emerging therapies in these regions.
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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 | 0.024 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.034 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".