Enhancing outcomes in the management of treatment resistant depression: a focus on atypical antipsychotics
Bibliographic record
Abstract
Clinical trials indicate that over 50% of depressed patients show an inadequate response to antidepressant therapy, and that incomplete recovery from major depressive disorder (MDD) increases the risk of chronicity and recurrence. Recovery, complete remission of symptoms, and a return to baseline psychosocial function, should be the goal of therapy. Poor response to adequate antidepressant treatment has been termed treatment resistant depression (TRD). Issues such as adherence, missed diagnosis of psychotic depression, bipolar disorder, or comorbid anxiety must be investigated as reasons why patients have not responded to initial therapeutic strategies. Beyond ensuring optimal use of the index antidepressant, treatment strategies for TRD include switching to another antidepressant, and augmentation or combination with two or more agents. Since little comparative data exist it is important to consider side-effect burden, partial response, and previous medication history when deciding between strategies. In patients with TRD, adding or augmenting with lithium, tri-iodothyronine or atypical antipsychotics have demonstrated benefits. Augmentation with atypical antipsychotics, including risperidone, olanzapine, ziprasidone, and quetiapine, show promising results in terms of improving remission rates. Other interventions, including non-pharmacologic strategies and investigational physical treatments, have demonstrated some benefits, but availability and patient preference should also be considered. With today's therapeutic alternatives, full remission of depression is an attainable goal. For some patients, combination and augmentation strategies earlier in treatment may increase the likelihood of remission.
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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 | 0.007 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".