Bibliographic record
Abstract
Amyloid light chain (AL) amyloidosis is a disorder characterized by the deposition of antibody light chains in organs. Early and accurate diagnosis of AL amyloidosis is crucial for timely implementation of appropriate treatment strategies. However, existing computational methods for predicting AL amyloidosis often heavily rely on manually extracted features and their performance is less than satisfactory. In this study, we introduce DeepAL, a deep learning-based approach designed to predict AL amyloidosis with high precision. DeepAL utilizes a pre-trained model to extract light chain features and is then fine-tuned with AL amyloidosis knowledge. On two benchmark datasets, DeepAL achieved impressive results with area under the ROC curves (AUCs) of 0.9072 and 0.8919, outperforming previous approaches. Our ablation study shows the use of the pre-trained model can significantly improve identification performance. The code is available at https://github.com/waterlooms/DeepAL.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".