Bibliographic record
Abstract
Research on personality disorders (PD) has made progress in several areas. 1) Classification: Most PDs are exaggerations of normal traits and can be described dimensionally, but highly symptomatic PDs may not be well described in this way. Attempts to redefine PDs as variants of Axis I categories, such as depression and bipolar disorder, have been unconvincing. 2) Epidemiology: Recent community surveys suggest that PDs as currently defined have an overall prevalence of around 10%. 3) Etiology: Studies of relationships between PDs and neuroimaging, neuropsychological testing, neurotransmitters, and candidate genes are not related specifically to disorders, although certain markers are linked to traits. The complex pathways to PDs depend on gene-environment interactions. 4) Outcome: PD categories tend to be unstable over time, largely because of symptom reduction. However the traits underlying disorders do not change, and many continue to have poor psychosocial functioning. 5) Treatment: Clinical trials show that a number of psychotherapeutic methods are effective, with improvement often occurring within a few months. In contrast, the results of pharmacological trials have been unimpressive and inconclusive.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".