Understanding real‐world treatment patterns and clinical outcomes in AL amyloidosis patients diagnosed in Canada: A population‐based cohort study
Bibliographic record
Abstract
Amyloid light chain (AL) amyloidosis is a rare and chronic bone marrow disorder. Existing claims data can be used to help understand the real-world treatment patterns and outcomes of this patient population. Various population-based administrative databases in Alberta, Canada were queried from 2010 to mid-2019 to identify cases of AL amyloidosis. Baseline patient and disease characteristics, sequencing of pharmacologic therapies, overall survival, and healthcare resource utilization were evaluated. A total of 215 individuals with AL amyloidosis were included. Among patients diagnosed between 2012 and 2019, 149 (85.1%) initiated first-line, 67 (38.3%) initiated second-line, 22 (12.6%) initiated third-line, and 11 (6.3%) initiated fourth-line systemic therapy. In the first-line setting, 99/149 (66.4%) received bortezomib, cyclophosphamide, and dexamethasone (CyBorD) and 21/149 (14.1%) received another bortezomib-based regimen. Survival from time of diagnosis improved over time, with a median overall survival of 25.8 months (95% CI: 9.8, 57.1) for individuals diagnosed in 2010-2011 versus 52.1 months (95% CI: 25.6, NA) for those diagnosed in 2012-2019. Despite this improvement, the proportion of individuals diagnosed in 2012-2019 who survived beyond five-years remained low (5-year survival: 48.4%; 95% CI: 40.9, 57.2) which highlights an unmet need for more efficacious therapies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".