Global epidemiology of amyloid light-chain amyloidosis
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
BACKGROUND: Amyloid light-chain (AL) amyloidosis is an ultra-rare disease associated with significant morbidity and mortality. Few studies have examined the global epidemiology of this condition. METHODS: This study estimated the diagnosed incidence and 1-year, 5-year, 10-year, and 20-year period prevalence of AL amyloidosis in 2018 for countries in and near Europe, and in the United States (US), Canada, Brazil, Japan, South Korea, Taiwan, and Russia. A systematic literature review (SLR) was conducted to identify country-specific, age- and gender-specific diagnosed incidence of AL amyloidosis and observed survival data-point inputs for an incidence-to-prevalence model. Extrapolations were used to estimate incidence and prevalence for countries without registry or published epidemiological data. RESULTS: Of 171 publications identified in the SLR, 10 records met the criteria for data extraction, and two records were included in the final incidence-to-prevalence model. In 2018, an estimated 74,000 AL amyloidosis cases worldwide were diagnosed during the preceding 20 years. The estimated incidence and 20-year prevalence rates were 10 and 51 cases per million population, respectively. CONCLUSIONS: Orphan medicinal product designation criteria of the European Medicines Agency or Electronic Code of Federal Regulations indicate that a disease must not affect > 5 in 10,000 people across the European Union or affect < 200,000 people in the US. This study provides up-to-date epidemiological patterns of AL amyloidosis, which is vital for understanding the burden of the disease, increasing awareness, and to further research and treatment options.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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