Optimizing a Clinical Trials Approach to Investigate Cognitive and Physical Health Outcomes in Late-Life Depression
Bibliographic record
Abstract
Late-life depression (LLD) is a common and multifaceted condition affecting approximately 10% of older adults. More so than depression in younger adults, LLD interacts with physical and cognitive health outcomes in both the short and the long term, necessitating a comprehensive approach to studying and treating this condition. There are bidirectional relationships between LLD and cognitive function and between LLD and physical function that are incompletely understood. Modern clinical trials in LLD need to address current gaps in our understanding of these links, and how they can be therapeutically targeted. The central aim of this Thesis was to narrow these knowledge gaps through targeted analyses of the extant literature and recent large clinical trial datasets, building the foundation for a pilot clinical trial design intended to launch a new direction of inquiry into the biological basis of LLD and its treatment. The central hypothesis underlying this aim was that LLD clinical trial design could be further optimized by an explicit focus on cognitive and physical health outcomes. This Thesis work employed a variety of methodological approaches, including systematic review and meta-analysis, exploratory data analysis, and multivariable linear modelling, with a transition towards primary trial design work in the final experimental chapter (Chapter 7). Where appropriate, both a priori and post-hoc subgroup analyses have been performed to better characterize various dimensions of this heterogeneous population, and for the purposes of further hypothesis generation. Several main findings have arisen from this Thesis work. First, LLD adversely affects cognition during the acute phase of illness, with impacts on executive function that are more pronounced in the setting of treatment resistance. Second, effective antidepressant treatment may lead to recovery from deficits in some cognitive domains but not others. Third, increased physical comorbidity adversely affects some areas of cognitive function and may negatively impact LLD treatment outcomes. And finally, the dopamine system is implicated in the links between LLD, cognition, and physical health, and this system may be pharmacologically manipulated to clinical benefit. This Thesis provides new insights into the biological underpinnings of LLD. It forms the basis for a promising new avenue of inquiry.
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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 | 0.251 | 0.343 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".