The inventory of psychotic-like anomalous self-experiences (IPASE): Stability and relationships with attenuated psychotic symptoms and remission in individuals at-risk for psychosis
Bibliographic record
Abstract
Background The IPASE is a self-report measure of basic self-disturbance, a core feature of schizophrenia and ultra-high risk (UHR) states. However, the extent to which basic self-disturbance—as captured by the IPASE—is stable over time and related to the severity or progression of attenuated psychotic symptoms (APS) remains unclear. We examined the temporal stability of IPASE scores, their correlation with APS, and whether they predict changes in APS over time. Methods The baseline sample included 185 participants (healthy controls = 72, UHR = 66, first-episode psychosis = 47), with 29 UHR participants re-assessed at month-12. Correlations between IPASE scores and Comprehensive Assessment of At-Risk Mental States (CAARMS) positive symptom scores were evaluated at baseline and month-12. Stability between baseline and month-12 IPASE scores was examined in the longitudinal subsample. Regression was used to predict remission and change in CAARMS scores. Results Although mean IPASE scores were significantly higher in the UHR group compared to HCs, total IPASE scores were only weakly correlated with CAARMS total scores ( ρ =0.27). IPASE subscales showed weak correlations (0.08< ρ <0.27) with CAARMS positive symptom domains. Changes in IPASE and CAARMS scores were not correlated. Moderate stability was found for IPASE total scores (ICC = 0.59) and four subscales (0.58 < ICC < 0.64), excluding the cognition subscale (ICC = 0.3). Baseline IPASE scores did not predict remission (partial R 2 =0.05) or change in CAARMS scores (partial R 2 =0.02). Conclusion The IPASE is a moderately stable measure in UHR individuals, correlates with the presence of positive psychotic symptoms but only weakly with severity, and does not strongly predict positive symptom change.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".