Do antidepressants influence mood patterns? A naturalistic study in bipolar disorder
Bibliographic record
Abstract
This prospective, longitudinal study compared the frequency and pattern of mood changes between outpatients receiving usual care for bipolar disorder who were either taking or not taking antidepressants. One hundred and eighty-two patients with bipolar disorder self-reported mood and psychiatric medications for 4 months using a computerized system (ChronoRecord) and returned 22,626 days of data. One hundred and four patients took antidepressants, 78 did not. Of the antidepressants taken, 95% were selective serotonin or norepinephrine reuptake inhibitors, or second-generation antidepressants. Of the patients taking an antidepressant, 91.3% were concurrently taking a mood stabilizer. The use of antidepressants did not influence the daily rate of switching from depression to mania or the rate of rapid cycling, independent of diagnosis of bipolar I or II. The primary difference in mood pattern was the time spent normal or depressed. Patients taking antidepressants frequently remained in a subsyndromal depression. In this naturalistic study using self-reported data, patients with bipolar disorder who were taking antidepressants--overwhelmingly not tricyclics and with a concurrent mood stabilizer--did not experience an increase in the rate of switches to mania or rapid cycling compared to those not taking antidepressants. Antidepressants had little impact on the mood patterns of bipolar patients taking mood stabilizers.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".