Bibliographic record
Abstract
Multiple sclerosis (MS) is the most common cause of neurological disability in young and middle-aged adults. Although Charcot noted behavioral changes associated with MS, nearly a century would elapse before researchers began defining their full extent and severity. Broadly speaking, abnormalities may be divided into those of mood and cognition. Many patients are afflicted with both and it is essential that clinicians are not only aware of this but understand how to detect problems and provide treatment. The lifetime prevalence of major depression in MS patients approaches 50%. As Scott B. Patten, MD, and colleagues note, these data came from specialist clinics with the potential for ascertainment bias. Shifting their inquiry into a large community-based sample, they report that the rates of mood disorder remain elevated largely in younger MS patients. While it is partly reassuring to find that aging comes with at least one benefit, the gist of this study is to reinforce the message that clinically significant depression is a problem for MS patients. Not only does it adversely affect quality of life and lead to increased suicidal thinking, it exerts more subtle deleterious effects as Peter A. Arnett, PhD, reveals. In a longitudinal study exploring the relationship between depression and cognition, Arnett reports that MS patients with prominent evaluative symptoms of depression (ie, feelings of inferiority, failure) have greater difficulty with cognitive tasks that encompass information processing speed and executive function linked to working memory. A preoccupation with negative thoughts may reduce the cognitive capacity necessary for aspects of attention and working memory. These data complement the review article of Ralph H.B. Benedict, PhD, ABPP-CN, that focuses on methods of detecting cognitive dysfunction in MS. As with mood disorders, impaired cognition has been linked to difficulties with work, relationships, and, in more extreme cases, basic activities of daily living. Usually, the more subtle pattern of deficits associated with demyelination differ from those seen in cortical-type dementias and will be missed should clinicians rely on screening instruments like the Mini-Mental State Examination. At the same time, the method of choice for eliciting deficits, namely neuropsychological testing is expensive and frequently not readily available. This has meant that alternative instruments, like the Multiple Sclerosis Neuropsychological Screening Questionnaire, assume an added prominence. With good sensitivity, specificity, and ease of administration this informant based scale makes a useful addendum to the neurological examination. It is in the same light that the magnetic resonance imaging (MRI) rating scale by Laury Chamelian, MD, FRCPC, and colleagues should be viewed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.017 |
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".