Laboratory Detection and Initial Diagnosis of Monoclonal Gammopathies
Bibliographic record
Abstract
CONTEXT.—: The process for identifying patients with monoclonal gammopathies is complex. Initial detection of a monoclonal immunoglobulin protein (M protein) in the serum or urine often requires compilation of analytical data from several areas of the laboratory. The detection of M proteins depends on adequacy of the sample provided, available clinical information, and the laboratory tests used. OBJECTIVE.—: To develop an evidence-based guideline for the initial laboratory detection of M proteins. DESIGN.—: To develop evidence-based recommendations, the College of American Pathologists convened a panel of experts in the diagnosis and treatment of monoclonal gammopathies and the laboratory procedures used for the initial detection of M proteins. The panel conducted a systematic literature review to address key questions. Using the Grading of Recommendations Assessment, Development, and Evaluation approach, recommendations were created based on the available evidence, strength of that evidence, and key judgements as defined in the Grading of Recommendations Assessment, Development, and Evaluation Evidence to Decision framework. RESULTS.—: Nine guideline statements were established to optimize sample selection and testing for the initial detection and quantitative measurement of M proteins used to diagnose monoclonal gammopathies. CONCLUSIONS.—: This guideline was constructed to harmonize and strengthen the initial detection of an M protein in patients displaying symptoms or laboratory features of a monoclonal gammopathy. It endorses more comprehensive initial testing when there is suspicion of amyloid light chain amyloidosis or neuropathies, such as POEMS (polyneuropathy, organomegaly, endocrinopathy, M protein, and skin changes) syndrome, associated with an M protein.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".