Keeping up with the clinical advances: depression
Bibliographic record
Abstract
Major depressive disorder (MDD) is a prevalent and heterogeneous disorder. Although there are many treatment options for MDD, patients with treatment-resistant depression (TRD) remain prevalent, wherein delayed time to response results in inferior chances of achieving remission. Recently, therapeutics have been developed that depart from the traditional monoamine hypothesis of depression and focus instead on the glutamatergic, GABAergic, opioidergic, and inflammatory systems. The literature suggests that the foregoing systems are implicated in the pathophysiology of MDD and preclinical trials have informed the development of pharmaceuticals using these systems as therapeutic targets. Pharmaceuticals that target the glutamatergic system include ketamine, esketamine, and rapastinel; brexanolone and SAGE-217 target the GABAergic system; minocycline targets the inflammatory system; and the combinatory agent buprenorphine + samidorphan targets the opioidergic system. The aforementioned agents have shown efficacy in treating MDD in clinical trials. Of particular clinical relevance are those agents targeting the glutamatergic and GABAergic systems as they exhibit rapid response relative to conventional antidepressants. Rapid response pharmaceuticals have the potential to transform the treatment of MDD, demonstrating reduction in depressive symptoms within 24 hours, as opposed to weeks noted with conventional antidepressants. Novel therapeutics have the potential to improve both patient mood symptomatology and economical productivity, reducing the debased human capital costs associated with MDD. Furthermore, a selection of therapeutic targets provides diverse treatment options which may be beneficial to the patient considering the heterogeneity of MDD.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".