Anxiety symptoms severity and short‐term clinical outcome in first‐episode psychosis
Bibliographic record
Abstract
AIM: In psychotic disorders, a limited number of studies have documented the presence of symptoms of anxiety, especially in first-episode psychosis (FEP). There is a growing interest in better understanding how these symptoms may affect the severity of psychotic symptoms and clinical outcome. This study examined the association between symptoms of anxiety, as measured by the Hamilton Anxiety Rating Scale (HARS) and the Positive and Negative Syndrome Scale (PANSS), and short-term clinical outcome. We first examined the potential association between anxiety symptom severity among FEP patients and remission. A secondary objective explored the relation between the PANSS single item subscale 'anxiety' item and the total score value of the HARS. METHOD: Data were collected on 201 FEP patients divided into remitted and unremitted groups based on clinical data at 6 months. Anxiety ratings were compared between 67 remitted and 99 unremitted patients with the HARS, and for 72 remitted and 103 unremitted patients with the (G2) PANSS. RESULTS: A significant interaction Time × Group was observed on the HARS and on the PANSS G2 item. Looking at the two time points specifically, groups did not significantly differ at baseline on either the HARS or the PANSS. At 6 months, these two groups were significantly different on both anxiety rating scores - HARS [t(170) = 3.48, P = 0.001)] and PANSS G2 [t(173) = 2.51, P = 0.013)]. CONCLUSION: Anxiety severity is marked in FEP, and appears to be linked to poor short-term clinical outcome. The PANSS single item (G2) seems to represent a good indicator of anxiety as it significantly correlates with a more systematic measure of anxiety, namely the HARS score. Anxiety severity appears to vary across diagnosis type.
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 | 0.003 |
| 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 | 0.000 |
| 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".