Application of Fava's semi-structured interview questionnaire in Chinese patients with depressive disorder and anxiety disorder
Bibliographic record
Abstract
Objective To study the prevalence of Fava's semi-structured diagnostic criteria for psychosomatic research(DCPR) clusters in a Chinese sample meeting the DSM-IV criteria for depressive disorder or anxiety disorders, and the association between three DCPR syndromes and three Chinese version scales, which are Toronto Alexithymia Scale (TAS-20), Type A Behavior Patern Scale (TABP) and Short Health Anxiety Inventory (SHAI). Methods 110 inpatients with depressive disorders and 41 with anxiety disorders were recruited. All subjects were administered the Chinese version of the semi-structured interview for DCPR, Hamilton depression scale (HAMD) and Hamilton anxiety scale (HAMA), and the prevalence of DCPR symptoms were compared with TAS-20, TABP and SHAI. Results 7 subjects (4.6%) did not satisfy the criteria for any DCPR syndrome, 39 subjects (25.8%) had one DCPR diagnosis, and 33 subjects (21.85%) had more than 5 DCPR syndromes. The 4 most common syndromes were alexithymia (83, 55.0%), demoralization (56, 37.1%), Type A behavior (52, 35.1%), and irritable mood (37, 24.5%). The prevalence of anniversary reaction in anxiety disorders (34.1%) was significantly different with that in depressive disorders (16.4%). There was no significant difference between alexithymia prevalence diagnosed by DCPR and by TAS-20. The prevalence of type A behavior diagnosed by DCPR was significantly different from by TABP (χ2=15.532, P=0.000). The prevalence of DCPR diagnosed health anxiety was significantly different from SHAI diagnosed (χ2=13.056, P=0.000). Conclusion 95.4% patients with depression or anxiety receive at least one DCPR diagnosis. TAS-20 and DCPR have high consistency in alexithymia diagnosis. Key words: Fava's Semi-Structured Diagnostic Criteria for Psychosomatic Research; Depressive disorders; Anxiety disorders; Alexithymia; Type A behavior
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".