Predictors of 6-month follow-up Outcome of Drug Treatment in Schizophrenia in a tertiary hospital of Bangladesh
Bibliographic record
Abstract
Background: Schizophrenia may have a better outcome in low- and middle-income countries. In Bangladesh short-term outcome of drug treatment of schizophrenia is also better. It is required to see the predictors of outcome of drug treatment of schizophrenia in Bangladesh. Objectives: General objective of this study is to assess the outcome of 6-month follow-up of patients with schizophrenia. Specific objective of this study is to find out the predictors of 6-month follow-up outcome of drug treatment in schizophrenia. Methods: Patients with a SCID-1/P diagnosis of schizophrenia (n=42) were assessed prospectively at baseline, at 6-week and at 6-month follow-up. Socio-demographic and relevant variables and questionnaire for family support and previous work record for the study were read in front of the patients and guardians and were filled up by the researcher. Psychopathological measurements were applied at base line by researcher and at 6-week and at 6-month by research assistant for the study population. Results: Follow-up data were available for 38 patients at 6-month and among them 86.85% achieved partial remission, 7.89% had not responded and 5.26% had relapsed. In multivariate analysis by General Linear Model Analysis of socio-demographic and relevant variables with the mean BPRS score as outcome in this study we found that age, education, marital status and history of previous work record were significantly associated with the 6-month treatment outcome. Conclusions: Drug treatment outcome of schizophrenia in Bangladesh is better in short-term follow-up. Increase family support and early management by drugs should be a target for intervention. Central Medical College Journal Vol 6 No 2 Jauly 2022 Page: 90-96
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".