Trends in Neuropsychiatric Terminology Use Within Registered Clinical Trials for Multiple Sclerosis: A Retrospective Descriptive Analysis
Bibliographic record
Abstract
Background/Objectives: People with multiple sclerosis (pwMS) are known to experience more neuropsychiatric (NP) conditions compared to the general population. Clinical trials are essential for deriving effective methods to manage these conditions in this patient population, thereby optimizing their quality of life. Here, we examined the temporal trends in the inclusion of NP terms in clinical trials of multiple sclerosis (MS) to provide insights into potential gaps in research. Methods: Using a custom Python-based program, we analyzed the inclusion of four a priori selected NP terms (fatigue, depression, pain, and anxiety) in the description section of clinical trials of MS registered in the clinicaltrials.gov database from January 2000 to October 2024. We investigated temporal trends by correlating unique mentions of NP terms with the trial start year and examined the association of trial factors with the inclusion of any or each NP term using separate multivariable logistic regression models. We further quantified the number of trials with NP terms that explicitly examined them as a study outcome. Results: Of the 2674 trials, 410 (15.3%) mentioned at least one of the four NP terms. Specifically, fatigue (n = 293 studies), depression (n = 115), pain (n = 98), and anxiety (n = 49) were mentioned. Overall, the probability of trials including fatigue, depression, or anxiety, but not pain, was found to increase over time. In multivariable regression, each 1-year increase in trial start year was associated with 6% (OR 1.06, 95%CI 1.03–1.09) higher odds of including at least one NP term. Industry funding was associated with 72% lower odds (OR 0.28, 95% CI 0.20–0.39) of including any NP term. Among trials that included at least one of the four NP terms, 69.4–80.5% of them explicitly studied these terms as an outcome. Conclusions: Interest has increased over time in incorporating considerations on NP comorbidity in trials of pwMS. Industry-funded trials are less likely to include these considerations, which suggests a potential gap in trial design and funding resource allocation.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".