Anti-Yo Antibody–Mediated Paraneoplastic Cerebellar Degeneration Associated with Cognitive Affective Syndrome in a Patient with Breast Cancer: A Case Report and Literature Review
Bibliographic record
Abstract
Breast cancer is the most common cancer in women, with 15%-25% of those tumours overexpressing the human epidermal growth factor receptor 2 (her2), which is associated with more aggressive disease. On rare occasions, patients present with a paraneoplastic syndrome months to years before their cancer diagnosis. Paraneoplastic cerebellar degeneration (pcd) is associated with fewer than 1% of cancers and is strongly associated with breast and gynecologic malignancies. Anti-Yo antibody is the antibody most frequently identified with the syndrome, and it is associated with a very poor prognosis. Recent studies have implicated a relationship between overexpression of her2 and anti-Yo-mediated pcd. Current pcd treatments include tumour removal, chemotherapy, targeted therapy, and immune-suppressive treatments. Outcomes of pcd are typically poor, and no guidelines for treatment currently exist. Early recognition followed by rapid initiation of treatment remains the cornerstone of therapy. Here, we present a case of anti-Yo-antibody pcd secondary to estrogen and progesterone receptor-negative, her2-positive breast cancer. Despite treatment with mastectomy, chemotherapy, and her2-targeted therapy, no significant neurologic improvement was achieved, and cerebellar cognitive affective syndrome subsequently developed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".