Apathy alters emotional arousal in chronic schizophrenia
Bibliographic record
Abstract
Background: Within the heterogeneity of schizophrenia, apathy constitutes an independent cluster of negative symptoms associated with poor outcomes. Attempts to identify an emotional deficit in patients who have schizophrenia with negative symptoms have yielded mixed results, and studies that focus on the relationship between apathy and emotional disorders are lacking. Methods: We set out to remedy this shortcoming using a validated battery of film excerpts to induce positive and negative emotions in patients with chronic schizophrenia with (n = 20) or without (n = 20) apathy, and in controls (n = 20) comparable for age, sex and socioeconomic status. We assessed emotions using an innovative but validated technique to evaluate tonic and phasic electrodermal activity and subjective feelings using a standardized visual analogue scale. Results: Using a qualitative measure of apathy, we did not find a specific decrease in tonic activity during the induction of positive emotions. However, we did observe that patients with apathy showed reduced tonic activity independent of valence (i.e., for both positive and negative emotions) compared with controls and patients without apathy. Moreover, the quantitative measure of apathy (Apathy Evaluation Scale) was the only significant factor, explaining 24% of the variance in tonic activity during induction of positive emotions after controlling for confounding factors. Limitations: Electrodermal activity was the only physiologic measure we acquired. We induced several emotions sequentially that might have overlapped with each other, but we added an emotional “washout” period and randomized the order of each film excerpt to limit this possibility. Conclusion: Taken together, these results suggest that apathy in schizophrenia could impair tonic activity during positive emotions. Treatments aimed at enhancing positive emotions may help alleviate apathy in schizophrenia.
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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 | 0.001 |
| 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) | 0.002 | 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".