Patterns and predictors of naturally occurring change in depressive symptoms over a 30-month period in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Depressive symptoms are common in multiple sclerosis (MS), yet there is little information about the pattern and predictors of changes in depressive symptoms over time. OBJECTIVE: We examined changes in depressive symptoms over a 30-month period and the demographic, clinical and behavioral predictors of such changes in relapsing-remitting MS (RRMS). METHODS: 269 persons with RRMS completed the Hospital Anxiety and Depression Scale (HADS) and a demographic/clinical scale, Godin Leisure-Time Exercise Questionnaire (GLTEQ) and Patient Determined Disease Steps (PDDS) scale every 6 months over a 30-month period. Data were analyzed using latent class growth modeling (LCGM). RESULTS: LCGM identified a two-class model for changes in HADS depression scores over time. Class 1 involved lower initial status (i.e. fewer depressive symptoms) and linear decreases in depressive symptoms over time (i.e. improving HADS scores), whereas Class 2 involved higher initial status (i.e. more depressive symptoms) and linear increases in depressive symptoms over time (i.e. worsening HADS scores). LCGM further indicated that being older (OR = 2.46; p < .05), married (OR = 2.62; p < .05), employed (OR = 4.29; p < .005) and physically active (OR = 2.71; p < .05) predicted a greater likelihood of belonging to C1 than C2. CONCLUSIONS: Depressive symptoms change over time in persons with RRMS, and the pattern of change can be predicted by modifiable and non-modifiable factors.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".