Bibliographic record
Abstract
Abbreviations AD antidepressant CANMAT Canadian Network for Mood and Anxiety Treatments MDD major depressive disorder TRD treatment-resistant depression Depression is a serious illness. Numerous studies show that major depressive disorder (MDD) is associated with significant personal distress and burden of disability.1 The good news is that there are many effective treatments available to treat MDD. The bad news is that not everyone experiences a full response to treatment. For example, the Sequenced Treatment Alternatives to Relieve Depression (commonly known by its acronym, STAR*D) effectiveness study found that, in the real world, remission outcomes are modest, with only 33% of patients with MDD achieving full remission of symptoms after the first antidepressant (AD) and only 67% after 1 year of treatment involving up to 4 treatment steps.2 Thus a significant percentage of patients will have a treatment-resistant depression (TRD), regardless of how treatment-resistant is defined.3 It is also clear that TRD is associated with poor outcomes and a disproportionate amount of the burden associated with MDD.4,5 Although TRD is often used in the literature as if it were a distinct entity, there is considerable variability in how TRD is defined and there is no consensus definition.6 For example, a commonly applied definition for TRD is failure of 2 or more ADs, preferably from different classes. However, some studies define resistant depression as failure of one AD, while others attempt to stage resistance by incorporating responses to other treatments (including older medications, such as tricyclic ADs or monoamine oxidase inhibitors, and somatic treatments, such as electroconvulsive therapy).7 Moreover, the definition of failure also varies considerably from study to study. Some TRD studies include patients with treatment failure by history, while others define failure prospectively. These diagnostic issues contribute to significant heterogeneity in TRD samples, which makes it difficult to compare treatment studies of TRD. In the context of modest success rates with AD monotherapy for MDD, how can we optimize outcomes for the large proportion of patients with TRD? We practice in an evidence-based medicine climate, so it is important to first consider evidence-based treatment approaches. However, we need to remember to differentiate between evidence of lack of efficacy and lack of evidence of efficacy. The latter is much more representative of the evidence landscape in psychiatry than the former. While there is reasonable evidence to support choices for initial pharmacotherapy of MDD, there is still only limited evidence for many important clinical questions, including how to manage poor or incomplete response to an initial AD. To further complicate the issue, the terminology for treatment strategies for TRD is changing. Augmentation and combination have been used in the literature to describe distinct strategies when adding another medication to an AD. Augmentation referred to adding a medication that was not considered an AD (for example, lithium or triiodothyronine), while combination referred to the practice of adding a second AD (for example, desipramine or bupropion). However, the definition of an AD is blurring and some medications that were considered augmentation agents may be effective ADs on their own. For example, older studies indicated that lithium may have acute AD effects for unipolar depression,8 and more recent studies show that some atypical antipsychotics (for example, quetiapine extended release9) are effective as monotherapy for nonpsychotic MDD. For this reason, some authors have suggested that the terms augmentation and combination be replaced by add-on or adjunctive, which do not infer the type of medication added.10 The 2009 revision of the Canadian Network for Mood and Anxiety Treatments (CANMAT) depression guidelines summarizes the evidence for pharmacologic strategies for TRD. …
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.004 |
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".