Rationale and Design of ANTHOLOGY: An ATTR Amyloidosis Real-World Evidence Program Aiming to Address Gaps in Amyloidosis Care
Bibliographic record
Abstract
INTRODUCTION: Patients with amyloid transthyretin (ATTR) amyloidosis typically experience rapid disease progression, poor treatment outcomes, irreversible loss of health-related quality of life (HRQoL), and premature mortality. Early diagnosis is vital. However, diagnostic delays and misdiagnosis are common due to under-recognition of early signs and symptoms. METHODS: ANTHOLOGY is an ATTR amyloidosis program, evidence generation, and quality improvement opportunity comprised of two multi-country, longitudinal, observational, real-world evidence studies: OverTTuRe (ClinicalTrials.gov identifier, NCT06355934) and MaesTTRo (NCT06465810). OverTTuRe will retrospectively extract and analyze secondary data from a broad spectrum of sources, and MaesTTRo will prospectively collect and analyze data from patient-reported outcome questionnaires, electronic health records, and insurance claims. PLANNED OUTCOMES: The primary objectives of OverTTuRe are to describe contemporary patient characteristics, treatment patterns and disease outcomes, and to characterize healthcare resource utilization (HCRU) and HRQoL in patients diagnosed with ATTR amyloidosis. Describing patient characteristics and HCRU before diagnosis is a secondary objective. The primary objectives of MaesTTRo are to describe patient characteristics, disease history and treatment patterns from diagnosis, and to prospectively define and assess the real-world effectiveness of current therapies. Secondary objectives are to compare the characteristics of patients according to the therapy received and compare the real-world effectiveness of current therapies. Exploratory objectives are to identify risk factors for disease progression and to describe healthcare costs. CONCLUSIONS: ANTHOLOGY aims to broaden understanding of the contemporary epidemiology of ATTR amyloidosis, identify opportunities to accelerate diagnosis, and assess real-world comparative effectiveness of treatments. This knowledge will be used to define world-class patient care, improve treatment outcomes and HRQoL, inform updates to clinical practice guidelines and treatment pathways, and transform ATTR amyloidosis management through evidence aimed at improving the quality of the current standard of care TRIAL REGISTRATION: ClinicalTrials.gov identifier, NCT06355934 (OverTTuRe) and NCT06465810 (MaesTTRo).
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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 | 0.147 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".