Temporal trends in mental health terminology in Alzheimer’s disease clinical trials
Bibliographic record
Abstract
BACKGROUND: Despite several studies having correlated Alzheimer's disease with mental health conditions, the extent to which they have been incorporated into Alzheimer's disease clinical trials remains unclear. OBJECTIVE: This study aimed to assess the temporal trends in mental health-related terminology in Alzheimer's disease clinical trials as a proxy measure of research interest. Additionally, it sought to determine the effect of the COVID-19 pandemic on the frequency of these terms through pre-pandemic and post-pandemic trend assessment. METHODS: In this retrospective descriptive analysis, we included 2243 trials with a start date between 1988 and 2022 by searching for the keyword "Alzheimer Disease" in the U.S. National Library of Medicine ClinicaTrials.gov database. A Python program was created to extract and count the frequency of four mental health terms (loneliness, depression, anxiety, and distress) by year and trial status (e.g., completed, active, recruiting). Binary logistic regression analyses were conducted to examine the yearly patterns in the appearance of the four mental health terms. A multivariable logistic regression analysis was performed to identify trial characteristics associated with each mental health term. RESULTS: Our results depicted a statistically significant increasing trend in three (i.e., loneliness, anxiety, distress) of the four mental health conditions by year. A comparison between pre-pandemic and post-pandemic trials showed an increase in the mention of the same three words over time. INTERPRETATION: These results may suggest a growing awareness of mental health conditions and a greater interest in considering these conditions in Alzheimer's disease trials, particularly after the onset of COVID-19. Future researchers should conduct more in-depth analyses to examine how mental health variables are operationalized in these trials, with consideration for their subsequent success.
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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 | 0.031 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".