A Comprehensive Multidisciplinary Diagnostic Algorithm for the Early and Efficient Detection of Amyloidosis
Bibliographic record
Abstract
Amyloidosis is a rare protein misfolding disease caused by the accumulation of amyloid fibrils in various tissues and organs. There are different subtypes of amyloidosis, with light chain (AL) amyloidosis being the most common. Amyloidosis is notoriously difficult to diagnose because it is clinically heterogeneous, no single test is diagnostic for the disease, and diagnosis typically involves multiple specialists. Here, we propose an integrated, multidisciplinary algorithm for efficiently diagnosing amyloidosis. Drawing on research from several medical disciplines, we have combined clinical decisions and best practices into a comprehensive algorithm to facilitate the early detection of amyloidosis. Currently, many patients are diagnosed more than 6 months after symptom onset, yet early diagnosis is the major predictor of survival. Our algorithm aims to shorten the time to diagnosis with efficient sequencing of tests and minimizing uninformative investigations. We also recommend typing and staging of confirmed amyloidosis to guide treatment. By reducing time to diagnosis, our algorithm could lead to earlier and more targeted treatment, ultimately improving prognosis and survival.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".