SMASH: An initiative for equitable access to precision medicine for rare or severe lipid disorders
Bibliographic record
Abstract
Background. Despite significant improvements in our knowledge of the biological basis of rare or severe lipid disorders and the refinement of their clinical management, equity challenges and barriers to access are gradually emerging, particularly in low-middle-income countries or remote regions. SMASH (System and Molecular Approaches of Severe Hyperlipidemia) is a global initiative with the goal of making precision medicine innovations available without discrimination for patients affected by rare or severe lipid disorders. Objectives. SMASH main objective is to facilitate access to accurate diagnosis and optimal treatment for patients affected by rare or severe lipid disorders regardless of where they live, their gender, ethnicity, or socioeconomic status. Overview. SMASH is an international initiative comprising five interrelated components: SMASH-Access, -Natural History, -Trials, -e-Share, and -Biorepository. SMASH has selected as templates four severe lipid disorders that have in common the accelerated development of precise diagnosis and the emergence of innovative treatments that represent equity challenges: HoFH (homozygous familial hypercholesterolemia), persistent chylomicronemia, LCAT (lecithin-cholesterol acyl transferase) deficiency, and severely elevated Lp(a). Access issues are broad and not limited to clinical or socio-economic factors. Several environmental variables are also contributory. Summary. SMASH is developed to conceive, support, or catalyze initiatives that might improve our understanding of rare or severe dyslipidemias and facilitate access to innovation for those affected. It will not duplicate ongoing initiatives but will support them. A system approach and a structured collaborative effort is mandatory to provide fair access to emerging treatments to patients in both developed countries and emerging economies.
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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 | 0.070 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.073 | 0.028 |
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".