Factors predicting the clinical course of generalised anxiety disorder
Bibliographic record
Abstract
BACKGROUND: Cross-sectional data show that generalised anxiety disorder (GAD) is a chronic condition with episodes lasting much longer than the six-month minimum required by DSM-III-R and DSM-IV. Although GAD is chronic, little is known about factors influencing illness duration. AIMS: To investigate variables that influence the clinical course of GAD. METHOD: A total of 167 patients with GAD participated in the Harvard-Brown Anxiety Research Program. Patients were assessed at intake and re-examined at six- to twelve-month intervals for five years. Kaplan-Meier curves were constructed to assess the likelihood of remission. Regression analysis was used to investigate factors predicting full or partial remission. RESULTS: The rate of remission was 0.38 after five years. Diminished likelihood of remission was associated with low overall life satisfaction, poor spousal or family relationships, a concurrent cluster B or C personality disorder and a low global assessment score. CONCLUSIONS: Full or partial remissions were less likely to occur in patients with poor relationships and personality disorders. These patients should be given more intensive and possibly multi-modal therapy.
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 | 0.007 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".