Dépersonnalisation—Données actuelles
Bibliographic record
Abstract
OBJECTIVE: Depersonalization is a fascinating clinical phenomenon referring to a self-consciousness disorder, characterized by emotional detachment from one's own feelings, thoughts, or actions. This article intends to summarize the current literature in this area. METHOD: Using the Medline data base, we reviewed literature addressing the clinical, etiology, nosology, physiopathology, and treatment of depersonalization. CONCLUSIONS: Derealization means that perception of the world and of external reality are altered. These 2 phenomena are often associated. They are not specific to any psychiatric entity and are reported in many different psychiatric syndromes. Many factors, including use of different substances, are involved in their onset. The physiopathology is still little known. However, some conceptual models suggest partial amygdala inhibition combined with activation of other amygdaloid structures. A serotoninergic functioning impairment is indicated in different pharmacologic studies. Different psychotropic drugs, especially serotoninergic antidepressants, have been proposed for pharmacotherapy; however, there are no conclusive randomized studies, and the contribution of psychotherapy in treating these patients is still questioned.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".