A correlation study of emotion recognition,alexithymia and flat affect in recovery schizophrenic patients
Bibliographic record
Abstract
Objective:To analyze the correlation between the facial emotion deficits and alexithymia or flat affect in patients with recovery schizophrenia. Methods:Eighty-two schizophrenic patients and eighty-eight healthy subjects were tested with the Chinese Facial Emotion Test (CFET) and Toron Alexithymia Scale(TAS-26),and rated on the flatten affective subscle of the SANS.Results: For recovery schizophrenic patients,the total correct score of and scores of recognition for each of six basic emotions were significantly less(P0.01),And the scores of factor Ⅰ,Ⅱ, or Ⅳ on TAS-26 were significantly more (p0.01)than that of controls. There was a significantly negative correlation between scores of CFET and the scores of factor Ⅰ,Ⅱ, or Ⅳ on TAS-26, in which 27 of 35 (71.4%) correlation coefficients reach statistic significance. And also a significantly negative correlation between some scores of CFET and flatten affective subscale of the SANS, in which 9 of 49 (18.4%) correlation coefficients reach statistic significance. There was no significant correlation between scores of TAS-26 and subscales of symptom, apart from subscale of eye attach.Conclusions: The impairment of facial emotion recognition as well as alexithymia indicated a special trait in recovery schizophrenics. Both of two symptoms may be involved in a common neural substrates, while the dissociation between alexithymia and flatten affection may suggested a different pathological emotion processing.
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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 | 0.001 | 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) | 0.002 | 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".