Bibliographic record
Abstract
This review describes the many neuropsychiatric abnormalities associated with multiple sclerosis (MS). These may be broadly divided into 2 categories: disorders of mood, affect, and behaviour and abnormalities affecting cognition. With respect to the former, the epidemiology, phenomenology, and theories of etiology are described for the syndromes of depression, bipolar disorder, euphoria, pathological laughing and crying, and psychosis attributable to MS. The section discussing cognition reviews the prevalence and nature of cognitive dysfunction, with an emphasis on abnormalities affecting multiple domains of memory, speed of information processing, and executive function. The detection, natural history, and cerebral correlates of cognitive dysfunction are also discussed. Finally, treatment pertaining to all these disorders is reviewed, with the observation that translational research has been found wanting when it comes to providing algorithms to guide clinicians. Guidelines derived from general psychiatry still largely apply, although they may not always be most effective in patients with neurologic disorders. The importance of future research addressing this imbalance is emphasized, for neuropsychiatric sequelae add significantly to the morbidity associated with MS.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".