Alcohol and substance use in older adults with treatment‐resistant depression
Bibliographic record
Abstract
INTRODUCTION: Alcohol and substance use are increasing in older adults, many of whom have depression, and treatment in this context may be more hazardous. We assessed alcohol and other substance use patterns in older adults with treatment-resistant depression (TRD). We examined patient characteristics associated with higher alcohol consumption and examined the moderating effect of alcohol on the association between clinical variables and falls during antidepressant treatment. METHODS: This secondary and exploratory analysis used baseline clinical data and data on falls during treatment from a large randomized antidepressant trial in older adults with TRD (the OPTIMUM trial). Multivariable ordinal logistic regression was used to identify variables associated with higher alcohol use. An interaction model was used to evaluate the moderating effect of alcohol on falls during treatment. RESULTS: Of 687 participants, 51% acknowledged using alcohol: 10% were hazardous drinkers (AUDIT-10 score ≥5) and 41% were low-risk drinkers (score 1-4). Benzodiazepine use was seen in 24% of all participants and in 21% of drinkers. Use of other substances (mostly cannabis) was associated with alcohol consumption: it was seen in 5%, 9%, and 15% of abstainers, low-risk drinkers, and hazardous drinkers, respectively. Unexpectedly, use of other substances predicted increased risk of falls during antidepressant treatment only in abstainers. CONCLUSIONS: One-half of older adults with TRD in this study acknowledged using alcohol. Use of alcohol concurrent with benzodiazepine and other substances was common. Risks-such as falls-of using alcohol and other substances during antidepressant treatment needs further study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".