Different levels of prepulse inhibition among patients with first-episode schizophrenia, bipolar disorder and major depressive disorder
Bibliographic record
Abstract
Background: Deficits in prepulse inhibition may be a common feature in first-episode schizophrenia, bipolar disorder (BD) and major depressive disorder (MDD). We sought to explore the levels and viability of prepulse inhibition to differentiate first-episode schizophrenia, BD and MDD in patient populations. Methods: We tested patients with first-episode schizophrenia, BD or MDD and healthy controls using prepulse inhibition paradigms, namely perceived spatial co-location (PSC-PPI) and perceived spatial separation (PSS-PPI). Results: We included 53 patients with first-episode schizophrenia, 30 with BD and 25 with MDD, as well as 82 healthy controls. The PSS-PPI indicated that the levels of prepulse inhibition were smallest to largest, respectively, in the first-episode schizophrenia, BD, MDD and control groups. Relative to the healthy controls, the prepulse inhibition deficits in the first-episode schizophrenia group were significant ( p < 0.001), but the prepulse inhibitions were similar between patients with BD and healthy controls, and between patients with MDD and healthy controls. The receiver operating characteristic curve analysis showed that PSS-PPI (area under the curve [AUC] 0.73, p < 0.001) and latency (AUC 0.72, p < 0.001) were significant for differentiating patients with first-episode schizophrenia or BD from healthy controls. Limitations: The demographics of the 4 groups were not ideally matched. We did not perform cognitive assessments. The possible confounding effect of medications on prepulse inhibition could not be eliminated. Conclusion: The level of prepulse inhibition among patients with first-episode schizophrenia was the lowest, with levels among patients with BD, patients with MDD and healthy controls increasingly higher. The PSS-PPI paradigm was more effective than PSC-PPI to recognize deficits in prepulse inhibition. These results provide a basis for further research on biological indicators that can assist differential diagnoses in psychosis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".