Pain perception in schizophrenia: influence of neuropeptides, cognitive disorders, and negative symptoms
Bibliographic record
Abstract
OBJECTIVES: The causes and nature of insensitivity to pain in schizophrenia remain unknown. The role of endorphins and the association of cognitive dysfunction and negative symptoms are postulated. METHODS: In this study, 43 patients with schizophrenia, five first-degree relatives, and 34 healthy controls were examined. Participants' plasma concentrations of substance P, β-endorphin, and calcitonin gene-related peptide (CGRP) were assessed. In patients, the Trail-Making Test, the Color Reading Interference Test (Stroop test), and the Positive and Negative Syndrome Scale Negative Syndrome subscale (PANSS N) test were performed. We also evaluated pain threshold using nociceptive reflex (RTIII) testing. RESULTS: The mean β-endorphin concentration was about 20% higher in patients than in healthy controls (P<0.05). CGRP concentrations were significantly higher in patients than in controls (5.34 ng/mL versus 4.16 ng/mL; P<0.01). Subjects treated with antipsychotic polytherapy had higher concentrations of CGRP than did patients treated with second-generation antipsychotic monotherapy (5.92 ng/mL versus 5.02 ng/mL; P<0.05). There were no correlations between any biochemical parameters and Trail-Making Test, Stroop test, and PANSS N scores. There were no differences in RTIII among study groups. Strong negative correlation (P<0.001) was found between PANSS N scores and subjective pain threshold on the right lower limb. CONCLUSION: The insensitivity to pain in schizophrenia is a complex phenomenon that is probably not related to changes in nociceptive pathways. Increase in β-endorphin level may be related to this issue, but it is uncertain if such concentration ensures analgesic effect. It is unknown if patients with schizophrenia in fact experience less pain. Cognitive impairment and excess negative symptoms may strongly influence the patient's expression of pain.
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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) | 0.001 | 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".