The Incidence Rate of AL Amyloidosis: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Introduction: AL amyloidosis is widely regarded as a rare disease, but characterization of its epidemiology has scarcely been reported. The purpose of this systematic review is to estimate the incidence rate of AL amyloidosis and examine population differences. Methods: MEDLINE, PubMed, and Google Scholar were searched from their inception until November 13, 2021 using search terms AL amyloidosis or immunoglobulin light-chain amyloidosis or light-chain amyloidosis or primary amyloidosis and incidence or epidemiology. Random-effects meta analysis of all cohort studies reporting an incidence rates for AL amyloidosis was performed. The quality of each study was assessed using a modified Newcastle-Ottawa scale. Subgroup analysis was performed based on geographical region. Results: Six studies with data from 2502 diagnosed cases of AL amyloidosis from 5 countries were included. The pooled incidence rate for AL amyloidosis was 10.48 per one-million person years (95% CI, 8.99 to 11.96). There was moderate heterogeneity in the data, which was eliminated with a subgroup analysis according to geographical region. AL amyloidosis was found to be more common in the Americas (incidence rate 11.52 per one-million person years, 95% CI 11.04 to 12.00) than in the Europe (9.10 per one-million person years, 95% CI, 7.62 to 10.58). Conclusion: There is a low worldwide incidence rate for AL amyloidosis, supporting the characterization of AL amyloidosis as a rare disease. The incidence of AL amyloidosis appears to vary in different populations, which suggests further well-designed studies are needed to elucidate underlying etiological factors and better inform clinical suspicion for AL amyloidosis.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".