Bibliographic record
Abstract
INTRODUCTION: The success of antidepressant research has long been challenged by a limited mechanistic understanding of depression pathogenesis and antidepressant treatment response. Progress in this field has thereby consistently been hindered by a lack of novel conceptual approaches and sophisticated experimental techniques to dissect the highly intricate neurobiology of depression. Using fresh approaches to investigate the cellular and molecular mechanisms underlying depression will thus be vital for discovery of novel antidepressant targets. AREAS COVERED: This article provides an overview of some fundamental problems that depression research is currently facing and critically evaluates how these issues could be addressed by future research. It also discusses novel conceptual and technological advances in the field of neuroscience, particularly in regard to how they may help in providing unprecedented insight into the molecular mechanisms of depression pathogenesis. EXPERT OPINION: Although progress in antidepressant drug discovery has been limited over recent years, modern innovations in neuroscience, molecular biology, genetics and bioinformatics are just beginning to revolutionize depression research and to reveal novel and promising treatment targets. Integrating findings from a range of relevant experimental models and using the most advanced technology will be vital for the future success of antidepressant drug discovery.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".