Latent class‐derived subgroups of depressive symptoms in a community sample of older adults: the Cache County Study
Bibliographic record
Abstract
OBJECTIVE: We sought to identify possible subgroups of elders that varied in depressive symptomatology and to examine symptom patterns and health status differences between subgroups. METHODS: The Cache County memory study is a population-based epidemiological study of dementia with 5092 participants. Depressive symptoms were measured with a modified version of the diagnostic interview schedule-depression. There were 400 nondemented participants who endorsed currently (i.e., in the past 2 weeks) experiencing at least one of the three "gateway" depressive symptoms and then completed a full depression interview. Responses to all nine current depressive symptoms were modeled using the latent class analysis. RESULTS: Three depression subgroups were identified: a significantly depressed subgroup (62%), with the remainder split evenly between a subgroup with low probability of all symptoms (21%), and a subgroup with primarily psychomotor changes, sleep symptoms, and fatigue (17%). Latent class analysis derived subgroups of depressive symptoms and Diagnostic and statistical manual of mental disorders, fourth edition depression diagnostic group were nonredundant. Age, gender, education, marital status, early or late onset, number of episodes, current episode duration, and functional status were not significant predictors of depression subgroup. The first subgroup was more likely to be recently bereaved and had less physical health problems, whereas the third subgroup were less likely to be using antidepressants compared with the second subgroup. CONCLUSIONS: There are distinct subgroups of depressed elders, which are not redundant with the Diagnostic and statistical manual of mental disorders, fourth edition classification scheme, offering an alternative diagnostic approach to clinicians and researchers. Future work will examine whether these depressive symptom profiles are predictive of incident dementia and earlier mortality.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".