Paraneoplastic Neurologic Disorders
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE OF REVIEW: To provide an overview and highlight recent updates in the field of paraneoplastic neurologic disorders. RECENT FINDINGS: The prevalence of paraneoplastic neurologic disorders is greater than previously reported and the incidence has been rising over time, due to improved recognition in the era of antibody biomarkers. Updated diagnostic criteria that are broadly inclusive and also contain diagnostic risk for clinical presentations (high and intermediate) and diagnostic antibodies (high, intermediate, and low) have replaced the original 2004 criteria. Antibody biomarkers continue to be characterized (e.g., KLHL-11 associated with seminoma in men with brainstem encephalitis). Some paraneoplastic antibodies also provide insight into likely immunotherapy response and prognosis. The rise of immune checkpoint inhibitors as cancer therapeutics has been associated with newly observed immune-mediated adverse effects including paraneoplastic neurological disorders. The therapeutic approach to paraneoplastic neurologic disorders is centered around cancer care and trials of immune therapy. The field of paraneoplastic neurologic disorders continues to be advanced by the identification of novel antibody biomarkers which have diagnostic utility, and give insight into likely treatment responses and outcomes.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it