Scales for Evaluating Depressive Symptoms in Chinese Patients With Schizophrenia
Bibliographic record
Abstract
There have been few studies evaluating depressive symptoms in Chinese patients with schizophrenia. Thus, we planned to compare the diagnostic validity of 4 commonly used assessment scales for depression in schizophrenia in China. The association between different depression scales and between depression scales and negative symptoms were also studied. The study population consisted of 101 inpatients meeting the DSM-IV criteria for schizophrenia. Depression in the study subjects was defined by the DSM-IV criteria for a major depressive episode. The negative subscale of the PANSS was used to assess the negative symptoms in schizophrenia. The following 4 depression scales were assessed for their diagnostic validity as measures of depressive disorder in schizophrenia: the Calgary Depression Scale for Schizophrenia (CDSS), the Montgomery-Asberg Depression Rating Scale (MADRS), the Hamilton Rating Scale for Depression (HAM-D), and the depression subscale of the PANSS (PANSS-D). The depression scales were found to be highly intercorrelated with each other. Of the 4 depression scales studied, only CDSS can discriminate between depression and a PANSS negative symptoms subscale score or negative item scores. The areas under the receiver operating characteristic curves of the CDSS, HAM-D, MARDS, and PANSS-D were 0.954, 0.881, 0.828, and 0.897, respectively. The area under the receiver operating characteristic curve of the CDSS was significantly greater than those of the HAM-D, the MARDS, and the PANSS-D. Our study suggests that the CDSS may provide optimal assessment of depression in patients with schizophrenia.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".