Polyneuropathy in Multiple Myeloma Patients Correlate with the Presence of Autoantibodies (P7.013)
Bibliographic record
Abstract
Background and goal of the study: Multiple myeloma is a lymphoproliferative disease often requiring neurotoxic treatment (thalidomide/bortezomibe). Patients may develop length dependent axonal polyneuropathy related with the treatment. Because of variable degree of neural involvement in patients with similar MM picture we analyzed the presence of different types of autoantibodies as a possible risk factor of more severe polyneuropathy. DESIGN/METHODS: We selected 36 consecutive patients (age: 55-71, 22 males and 14 females) referred to our clinic because of paresthesias between 2008-2011 with one year history of MM treated with thalidomide/bortezomibe. All of them were examined clinically (Toronto Clinical Neuropathy Score, TCNS) and electrophysiologically. We examined the presence of autoantibodies using indirect immunofluorescence (IIF, Euroimmun, Germany) as a screening, and in cases with positive pattern indicating the presence of onconeural antibodies line blot (Euroimmun, Germany)was performed as the confirmation test. IIF enables detection of a spectrum of autoantibodies (anti-myelin associated glycoprotein - MAG, anti-glutamic acid decarboxylase - GAD, anti-glial fibrillary acid protein - GFAP, anti-gangliosides, anti-neuroendothelium, anti-myelin). RESULTS: We found autoantibodies in 20 of them (55%) - 12 (33%) had antibodies against nucleosome antigens (ANA), 4 (11%) had anty-PNMa2/Ta(+/-), 2 (5.5%) anti-myelin and 2 (5.5%) had abnormal granular and molecular layer of cerebellar cortex staining. Mean TCNS score in patients with autoantibodies was significantly higher (mean= 7.5, SD=1.8) than in patients without autoantibodies (mean=3.5, SD 0,7) t-Student test, p<0,05. TCNS correlated with the severity of electrophysiological abnormalities (length-dependent axonal sensory [6 patients] or sensory-motor [14 patients]); no motor involvement was found in the 16 patients without autoantibodies. . CONCLUSIONS: MM patients who develop autoantibodies have higher risk of more severe polyneuropathy with motor fiber involvement in comparison to those without immunological response. EMG-ENG assessment as well as autoantibodies analysis prior to potentially neurotoxic therapy initiation may be helpful in choosing the appropriate drug.
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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 | 0.000 | 0.000 |
| 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) | 0.004 | 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".