Bibliographic record
Abstract
BACKGROUND: An association between multiple sclerosis (MS) and depression has been recognized for several decades and has attracted considerable attention in research. However, there are considerable gaps in the current state of knowledge. In this review, the literature concerned with: (1) the burden of depression in MS; (2) the etiology of depression in MS, and (3) the treatment of depression in MS are critically examined. METHOD: The literature review utilized Medline (1966-1996), and was supplemented by citations extracted from the papers originally uncovered. RESULTS: Numerous studies have identified elevated depressive symptom scores in MS patients relative to nonclinical and (some) clinical control groups. Furthermore, studies of depressive disorders have clearly documented elevated prevalence rates in MS samples. The literature does not identify any specific pattern of neurological involvement as being consistently associated with depressive symptoms or disorders. Psychosocial risk factors contribute to the etiology of depression in MS, but the relative importance of various risk factors is yet to be determined. A single randomized controlled clinical trial, and additional anecdotal evidence, suggests that antidepressant pharmacotherapy is effective for depressive disorders in MS. CONCLUSIONS: Future epidemiological studies should not restrict their evaluation of risk factors to those specific factors that are closely related to the disease process. In particular, future researchers should resist the temptation to focus too exclusively on neuropathology. Biological, psychological and social risk factors are all potentially important. Additional empirical efforts to refine the various treatment approaches would be a welcome addition to this literature.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".