Alteration of pain recognition in schizophrenia
Bibliographic record
Abstract
BACKGROUND: Schizophrenia patients display impaired recognition of their own emotions and those of others and deficits in several domains of empathy. The first-person experience of pain and observing others in pain normally trigger strong emotional mechanisms. We therefore hypothesized that schizophrenia patients would display impaired recognition and categorization of both their own pain and the pain of others. METHODS: We studied 29 patients (18 men/11 women; 36 ± 13 years old) with paranoid schizophrenia-spectrum disorder and 27 healthy volunteers (20 men/7 women; 31 ± 9 years old) matched for age, gender, IQ and socio-cultural level. We assessed symptom severity and theory of mind. The participants' ability to detect and categorize pain in others was assessed with the sensitivity to expressions of pain (STEP) test, which is based on facial expressions, and another dynamic test involving a series of video sequences showing various pain-inducing events. The ability of patients to evaluate their own pain was assessed with the situational pain questionnaire (SPQ), which includes a series of questions assessing how one would expect to feel in different imaginary situations. Empathic tendencies were assessed with the interpersonal reactivity index. RESULTS: Patients and controls differed significantly in STEP, pain video and SPQ scores. By contrast with control subjects, the patients' pain judgements were not correlated with their affective or cognitive empathic capacities. CONCLUSIONS: Schizophrenic patients have a deficit of the identification and categorization of pain both in themselves and in others.
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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 | 0.000 | 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".