Laboratory Workup of Amyloidosis
Bibliographic record
Abstract
Context.—: Treatments are available for common forms of systemic amyloidosis that show promise for extending and improving the quality of life for patients. Early diagnosis and accurate identification of amyloid fibril type are crucial for successful treatment, but the diagnosis and workup of amyloidosis is inconsistent among pathologists and laboratories. Thus, the goal of this guideline is to offer recommendations for proper testing and workup for amyloidosis to optimize patient care. Objective.—: To establish evidence-based recommendations for appropriate laboratory testing to detect amyloidosis and identify the specific amyloidogenic protein. Design.—: The College of American Pathologists convened a panel of experts to develop recommendations following the standards established by the National Academy of Medicine for developing trustworthy clinical practice guidelines. The panel conducted a systematic literature review addressing 6 key questions. Using the Grading of Recommendations Assessment, Development and Evaluation framework, recommendations were created based on the available evidence, certainty of that evidence, and key judgments as defined in the framework. Results.—: Four conditional recommendations and 3 good practice statements were established to provide guidance for proper testing and workup of amyloidosis. Conclusions.—: This guideline summarizes the available evidence on the diagnosis and workup of systemic amyloidosis in tissue samples, including the challenges and limitations of common approaches and techniques. Recommendations for pathologists and laboratories receiving these samples are provided.
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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 | 0.018 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".