Bibliographic record
Abstract
Major depressive disorder is frequently undiagnosed and untreated in older patients. Grief, pain, sleep issues, concurrent medications, altered physiology, and the presence of comorbid medical and psychiatric conditions can complicate the management of depression in older patients. Remission should be the goal of therapy in treating depression in the elderly, just as it is in younger patients, to maximize the impact of treatment on quality of life. Managing depression in older patients can be done effectively with the antidepressant therapies currently available, including selective serotonin reuptake inhibitors (SSRIs), venlafaxine, and mirtazapine. Comorbid medical conditions, which are common among older patients, can have a significant impact on depression and vice versa. Antidepressant therapy with SSRIs has demonstrated efficacy and tolerability in patients at high risk for cardiovascular events and stroke and in those with vascular dementia or Alzheimer's disease. Care should be taken to choose antidepressants with no or minimal effects on glucose levels in patients with diabetes. In addition, venlafaxine has demonstrated beneficial effects on the relief of the pain of diabetic neuropathy. Venlafaxine, mirtrazapine, and the SSRIs have demonstrated efficacy and tolerability in older patients, while tricyclic antidepressants have also demonstrated efficacy; however, tolerability can be a problem. Depression is not a natural part of the aging process, as some still believe. The review of current data indicates that the goal of management can and should be full remission. Further, the use of newer agents is safe and effective in this population, as long as one considers the pharmacokinetics and pharmacodynamic properties and inherent biological differences in the elderly population when selecting appropriate therapy.
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 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
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".