ROLE OF BIOFEEDBACK THERAPY FOR REDUCING AFFECTIVE SYMPTOMS OF SCHIZOPHRENIA: A HOSPITAL-BASED COMPARATIVE STUDY.
Bibliographic record
Abstract
Background Schizophrenia is a debilitating disorder making it a challenge for clinicians to manage its heterogeneous symptom profile. Antipsychotics remain the main modality of its treatment. However, some symptoms persist after an optimal dose of antipsychotics. The affective and cognitive symptoms need a holistic approach for resolution. Biofeedback is a noninvasive procedure showing its effectiveness in various mental illnesses. Integration of biofeedback adjunctive to medications can help in attaining treatment goals in schizophrenia. Objectives It is to determine the role of biofeedback as an adjunctive therapy technique to traditional pharmacotherapy in patients with schizophrenia in improving affective symptoms. Methodology Sixty patients diagnosed with schizophrenia were selected for the study after fulfilling the inclusion and exclusion criteria. Patients were allotted to either the test group or control group by alternate allocation method. All patients were titrated to an optimal fixed dose of antipsychotic medications during 1st week of allotment to study. Biofeedback therapy was given 3 sessions per week for 3 weeks to patients in the test group. Positive and negative syndrome scale (PANSS) was applied for all patients at baseline. Hamilton Anxiety Rating Scale (HAM-A) and Calgary Depression Scale for Schizophrenia (CDSS) were applied at baseline and after 4 weeks to measure anxiety and depressive symptoms respectively. After 4 weeks the results were compared between both groups using statistical analysis. Conclusion The outcome of the study found that biofeedback therapy is effective in reducing anxiety and depressive symptoms in patients receiving biofeedback therapy compared to those who did not receive it. Recommendation Further studies in a larger cohort are needed to formulate sound and consistent conclusions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".