P.029 Facial onset sensorimotor neuronopathy syndrome (FOSMN) associated with Non-Hodgkin Lymphoma (NHL)
Bibliographic record
Abstract
Background: FOSMN is a recently describe neurological syndrome, characterizes by slow onset of facial sensory abnormalities and motor deficits. The initial description showed a very uniform clinical presentation. Since the initial description there are clinical cases describe in literature with subtle phenotype variations. Methods: We describe a clinical case associated with NHL. We will report clinical data, laboratory and neurophysiological findings. Results: Patient initiated with left perioral and mental sensory symptoms on her left side. It spread up to include left V2 area and spread to the right side. After 2 years she developed sensory symptoms on her right hand. Progressed to weakness and atrophy on the right upper limb. Also developed dysarthria, dysphonia, dysphagia, as well as photophobia, anisocoria and double vision. Had thorough work-up and everything unrevealing. Except for Spep that showed increased free kappa. Bone marrow biopsy showed evidence of a clonal cell expansion consistent with indolent lymphoma Conclusions: This case provides evidence of FOSMN associated with NHL. To our knowledge this is a first case describe with NHL. There had been reports with motor neuro diseases phenotype with lymphoma that may represent a paraneoplastic disorder. Our patient expands the clinical presentation. This finding should not lessen the diagnosis of FOSMN.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".