Comparison of social cognition using an adapted Chinese version of the Reading the Mind in the Eyes Test in drug-naive and regularly medicated individuals with chronic schizophrenia and healthy controls in rural China
Bibliographic record
Abstract
Abstract Background Social cognition has not previously been assessed in treatment-naive patients with chronic schizophrenia, in patients over 60 years of age, or in patients with less than 5 years of schooling. Methods We revised a commonly used measure of social cognition, the Reading the Mind in the Eyes Test (RMET), by expanding the instructions, using both self-completion and interviewer-completion versions (for illiterate respondents), and classifying each test administration as ‘successfully completed’ or ‘incomplete’. The revised instrument (RMET-CV-R) was administered to 233 treatment-naive patients with chronic schizophrenia (UT), 154 treated controls with chronic schizophrenia (TC), and 259 healthy controls (HC) from rural communities in China. Results In bivariate and multivariate analyses, successful completion rates and RMET-CV-R scores (percent correct judgments about emotion exhibited in 70 presented slides) were highest in HC, intermediate in TC, and lowest in UT (adjusted completion rates, 97.0, 72.4, and 49.9%, respectively; adjusted RMET-CV-R scores, 45.4, 38.5, and 34.6%, respectively; all p < 0.02). Stratified analyses by the method of administration (self-completed v. interviewer-completed) and by education and age (‘educated-younger’ v. ‘undereducated-older’) show the same relationship between groups (i.e. NC>TC>UT), though not all differences remain statistically significant. Conclusions We find poorer social cognition in treatment-naive than in treated patients with chronic schizophrenia. The discriminant validity of RMET-CV-R in undereducated, older patients demonstrates the feasibility of administering revised versions of RMET to patients who may otherwise be considered ineligible due to education or age by changing the method of test administration and carefully assessing respondents' ability to complete the task successfully.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".