Bibliographic record
Abstract
M ood disorders are among the leading causes of morbid- ity and disability worldwide.They are also associated with increased risk of mortality due to physical complications.Much research has been carried out to map susceptibility genes and to understand the pathophysiology of the illness.Yet, progress has been slow.This is partly explained by the obvious complexity of brain function and mood regulation; however, another reason for the lack of progress may be clinical.This month's "In Review" section is dedicated mainly to clinical research in mood disorders.Not surprisingly, many of the issues addressed by these reviews have been studied before and have produced controversial findings.As the papers in this issue demonstrate, large samples and careful scrutiny of relevant factors are needed to arrive at unequivocal conclusions.
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 | 0.067 | 0.205 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.028 | 0.030 |
| Insufficient payload (model declined to judge) | 0.063 | 0.038 |
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".