The Influence of Duration of Disease on Clinical Characteristics in Schizophrenia Patients
Bibliographic record
Abstract
Object: Schizophrenia affects 1% of the population and the clinical course is highly variable.In this study, we aimed to compare the clinical characteristics of patients with schizophrenia longer than 10 years and longer than 15 years in order to contribute to the identification of the clinical appearance of schizophrenia in the prolonged disease process.Methods: 71 patients who were diagnosed with schizophrenia for a short period of 10 years and 53 patients who were followed for a period of 15 years were included.All of the patients were assessed by the Positive and Negative Syndrome Scale (PANSS), the Calgary Schizophrenia Depression Scale (CSDS), the Quality of Life Enjoyment and Satisfaction Questionnaire (Q-LES-Q), the Extrapyramidal Symptom Rating Scale The UKU (UgvalgforKliniskeUndersgelser) Side Effect Rating Scale, the Schedule for Assessing the Three Components of Insight and the Morisky Treatment Compliance Scale (MCQS) were administered.Results: The scores of PANSS Negative symptoms subscale, CBSS, BPRS and MTUS scores were statistically significantly higher in the patient group with a disease duration of 15 years or more than in the group with a disease duration of 10 years or less.When the neurological subscale scores of the UKU side effect assessment scale were compared between the two groups; they were statistically significantly higher in the patient group with a disease duration of 15 years or longer.Conclusion: As a result of this study, it has been shown that, in patients with schizophrenia, the longer the illness is, the more negative and depressive symptoms increase, as well as the insight into the patient's illness and the increased drug compliance.It is seen that there is a need for larger scale, prospective planned studies for schizophrenic patients with prolonged disease duration, to understand what is going on schizophrenia and for factors to be taken into account in such cases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".