Bibliographic record
Abstract
Major depressive disorder (MDD) is a chronic disorder that substantially impairs a patient's psychosocial and occupational functioning. Lifetime prevalence rates for MDD vary widely, ranging from 4.4% to approximately 20%, and it is predicted to become the second leading cause of disability by the year 2020. The magnitude of this public health problem, with its associated decreased quality of life, increased risk of suicide, loss of productivity, and increased health care use, underscores the importance of treating depressed patients to full remission. The presence of residual depressive symptoms due to partial or incomplete remission is associated with significant morbidity and mortality. Hence, complete remission should be the goal in the treatment of patients with MDD because it leads to a symptom-free state and a return to premorbid levels of functioning. Full remission and improved long-term prognosis can be achieved with long-term antidepressant therapy with newer agents that work through multireceptor mechanisms, especially through the serotonergic and noradrenergic systems (i.e., dual action). Robust efficacy and greater remission rates have been associated with dual-action agents.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".