Chemotherapy‐based approach is the preferred treatment for sporadic late‐onset nemaline myopathy with a monoclonal protein
Bibliographic record
Abstract
Sporadic late-onset nemaline myopathy (SLONM) associated with monoclonal protein (MP) is a rare disease with an aggressive, and often fatal course. Whether SLONM + MP represents a malignancy or dysimmune disease remains unclear. Currently, two main approaches are used to treat SLONM + MP: nonchemotherapy-based treatment (immunosuppression, intravenous immunoglobulins, plasmapheresis and plasma exchange) or chemotherapy with or without autologous stem cell transplantation. Due to the rare occurrence of the disease, the best treatment modality is unknown. We analyzed treatment and outcomes in a large cohort of 53 patients with SLONM + MP: four our own patients and 49 cases from published literature. Neurological improvement in the nonchemotherapy group (N = 25) was observed in 52% of patients: 8% reached marked improvement, 8% moderate response, 36% mild response; none reached complete remission (CR). In the chemotherapy group (N = 28), neurological improvement was seen in 86% of patients: 46% reached CR, 25% marked response, 11% moderate response and 4% mild response. The best neurological improvement correlated with deep hematological remission. Mean time to best response in the chemotherapy group was 8 months versus 21 months in the nonchemotherapy group (P < .001). Overall survival was higher in patients in the chemotherapy group. A chemotherapy approach should be the preferred treatment for patients with SLOMN + MP with the goal to reach complete hematologic remission. Based on the clinical, morphological peculiarities, aggressive disease course and superior clinical benefits of chemotherapy over nonchemotherapy, SLONM + MP should be considered as a hematological malignancy with the presence of MP of clinical rather than undetermined significance.
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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".