Nonresponse to first-line pharmacotherapy may predict relapse and recurrence of remitted geriatric depression
Bibliographic record
Abstract
The authors examined whether nonresponse to first-line pharmacotherapy was associated with an increased probability of relapse or recurrence following remission of an episode of geriatric depression. The study group consisted of 74 elderly patients whose index episode of nonpsychotic unipolar major depression had responded to antidepressant pharmacotherapy. In 6 of these patients, the depressive episode had not responded to first-line pharmacotherapy (8 weeks of nortriptyline, including 2 weeks of adjunctive lithium) but it had responded to second-line treatment (phenelzine with or without adjunctive lithium). The 74 patients were maintained on acute doses of the medications that had led to response and were followed for 2 years or until relapse or recurrence, whichever occurred first. The cumulative probability of relapse or recurrence was 67% for patients who responded to second-line treatment compared with 18% for patients who responded to first-line treatment (P = 0.0003). As expected, mean time to response was significantly longer for patients who responded to second-line treatment but this factor did not account for the difference in outcome between the two groups. These findings suggest that pharmacotherapy resistance may constitute a risk factor for relapse or recurrence of remitted geriatric depression, even when patients are maintained on the medication that they eventually respond to.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".