Antidepressant Combinations: Widely Used, but Far from Empirically Validated
Bibliographic record
Abstract
This paper reviews the evidence on combining antidepressants (ADs) for treatment of major depressive disorder. Although widely used and usually safe, the efficacy of even the most widely prescribed combinations of ADs has not been established by properly controlled, adequately powered, clinical trials. This stands in contrast to several adjunctive strategies for AD nonresponders, including adjunctive lithium, thyroid hormone, or newer-generation antipsychotics. The wide use of AD combinations no doubt reflects the limited efficacy of commonly used ADs and the unmet need for effective strategies for patients with treatment-resistant depression. Although of unproven efficacy, potential merits of combining selected ADs include: (1) avoiding discontinuation-emergent symptoms and cross-titration schedules, (2) at worst, the second AD should be as effective in combination as it would be as a monotherapy following a switch, and (3) the possibility of complementary neuropharmacologic effects that may enhance efficacy or improve tolerability. The dearth of controlled studies of such a commonly used strategy for such a highly prevalent condition is symptomatic of shortcomings in the way clinically relevant research is funded, points to the need for industry-academic-federal collaborations, and underscores the need for large, practice-based, research groups that can efficiently complete publicly funded studies of high public health impact.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".