Bibliographic record
Abstract
PURPOSE OF REVIEW: The aim of this article is to review opsoclonus, with particular emphasis on its immunopathogenesis and pathophysiology. RECENT FINDINGS: Infections (West Nile virus, Lyme disease), neoplasms (non-Hodgkin's lymphoma, renal adenocarcinoma), celiac disease, and allogeneic hematopoietic stem cell transplantation can cause opsoclonus. Newly identified autoantibodies include antineuroleukin, antigliadin, antiendomysial, and anti-CV2. Evidence suggests that the autoantigens of opsoclonus reside in postsynaptic density, or on the cell surface of neurons or neuroblastoma cells (where they exert antiproliferative and proapoptotic effects). Most patients, however, are seronegative for autoantibodies. Cell-mediated immunity may also play a role, with B and T-cell recruitment in the cerebrospinal fluid linked to neurological signs. Rituximab, an anti-CD20 monoclonal antibody, seems efficacious as an adjunctive therapy. Although changes in synaptic weighting of saccadic burst neuron circuits in the brainstem have been implicated, disinhibition of the fastigial nucleus in the cerebellum, or damage to afferent projections to the fastigial nucleus, is a more plausible pathophysiologic mechanism which is supported by functional magnetic resonance imaging findings in patients. SUMMARY: There is increasing recognition that both humoral and cell mediated immune mechanisms are involved in the pathogenesis of opsoclonus. Further studies are needed to further elucidate its immunopathogenesis and pathophysiology in order to develop novel and efficacious therapy.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".