Monoclonal Gammopathies of ‘Neurological Significance’: Paraproteinemic Neuropathies
Bibliographic record
Abstract
OBJECTIVES: To study the clinical profile and outcomes of patients with paraproteinemic neuropathy (PPN) and to explore the utility of nerve conduction studies (NCSs) to differentiate between the demyelinating subtypes. METHODS: We did a retrospective analysis of patients diagnosed with PPN between January 2010 and December 2019 in an inpatient setting. The study population consisted of patients above 16 years of age presenting with clinical features suggestive of chronic peripheral neuropathy and on evaluation was found to have PPN. RESULTS: A total of 74 patients were identified. The patients were predominantly in the 6th decade, and the majority were males. The subtypes of PPN were monoclonal gammopathy of undetermined significance (MGUS) (45.9%), POEMS syndrome (polyneuropathy, organomegaly, endocrinopathy, monoclonal plasma cell disorder, and skin changes) (24.3%), solitary plasmacytoma (17.6%), multiple myeloma (8.1%), and AL amyloidosis (4.1%). There are specific features on NCS which can help in identifying POEMS syndrome and IgM MGUS. The majority of patients with PPN tend to stabilize or improve with treatment; however, many have a severe residual disability. New terminology and classification of these entities as 'monoclonal gammopathies of neurological significance' can aid in early diagnosis and the development of effective treatment, to prevent residual disability. CONCLUSION: PPN has a heterogeneous spectrum of clinical, biochemical, and electrophysiological features. NCS can help distinguish POEMS syndrome and IgM MGUS from other demyelinating subtypes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".