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Record W4415241171 · doi:10.3390/healthcare13202593

Trends in Neuropsychiatric Terminology Use Within Registered Clinical Trials for Multiple Sclerosis: A Retrospective Descriptive Analysis

2025· article· en· W4415241171 on OpenAlexaff
Braxton Phillips, Harasees Singh, Maya Morcos, Amir‐Ali Golrokhian‐Sani, Marc Morcos, Rui Fu

Bibliographic record

VenueHealthcare · 2025
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of OttawaUniversity of Calgary
Fundersnot available
KeywordsClinical trialTerminologyLogistic regressionOddsAnxietyDescriptive statisticsOdds ratioDepression (economics)

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.029
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.579
GPT teacher head0.517
Teacher spread0.062 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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