Bibliographic record
Abstract
It is now accepted that major depressive disorder (MDD) is not a single pathophysiological entity. It is therefore not surprising that remission rates to a first antidepressant trial are low. In addition, antidepressants may target various neuronal elements for which there are gene polymorphisms, such as the serotonin (5-HT) reuptake transporter, which may modulate response. Acting on a single monoaminergic target, such as inhibiting the 5-HT transporter, may confer efficacy in MDD, but other targets may be used and/or combined in treatment-resistant patients. These include the blockade of norepinephrine transporters, monoamine oxidase, 5-HT(2A), 5-HT(1B) and 5-HT7 receptors, and the activation of 5-HT(1A) and dopamine 2 receptors. While antidepressants may have more than one of these properties, so do atypical antipsychotics. When using the latter medications, however, their regimens should be below those effective in treating psychosis to avoid dopamine 2 antagonism, which could be counter-productive in MDD. In some patients, combining medications from treatment initiation may also provide additional therapeutic benefits.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".