Bibliographic record
Abstract
Mood disorders comprise major depressive disorder (MDD), bipolar disorder (BD) and the milder forms of these two disorders. Reccurring MDD is also known as unipolar disorder. The distinction between unipolar and bipolar disorders was first suggested in 1957 by Leonard and was made official after support by several studies in 1980. Indeed, in 150 AD, Aretaeus of Cappadocia wrote "It seems to me that melancholia is the beginning and a part of mania". Additionally, Kraepelin, who proposed the first medical disease model in psychiatry a century ago, considered recurrent unipolar depression cases under the category of bipolar disorder and conceptualized spectrum disorders. Because today's classification systems conduct cross-sectional diagnosis, they do not consider family history, long-term characteristics and multidimensional approaches on symptoms. This method prioritizes reliability over validity and it rules out psychiatric disorders in etiology. Actually, a spectrum model which covers physical diseases is conceivable. The concept of epigenetics considers mood disorders, Alzheimer's disease, attention deficit and hyperactivity disorder, Carney syndrome, multiple endocrine neoplasia type I and II, breast and prostate cancers, carsinoid tumors, cerebrovascular and cardiovascular diseases and metabolic syndrome together. This review addressed the relationship between metabolic syndrome and mood disorders in this context along with genetic, clinical and environmental factors such as climate, geographic factors, migration and changeable lifestyles. Genetic and clinical variables are affective temperament, childhood trauma and use of antidepressants and antipsychotics.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".