The Side Effect of Haloperidol in Schizophrenic Patients: Analysis of Red Blood Cell Distribution Width (RDW) and Mean Platelet Volume (MPV) Values
Bibliographic record
Abstract
INTRODUCTION: Like the increase of pro-inflammatory cytokines and oxidative stress as schizophrenia pathophysiology, haloperidol also increases RDW and MPV values. Both of these values have been clinicians concern because they are a risk factor for the various type of vascular disease. OBJECTIVE: This study aims to determine the side effect of haloperidol on RDW and MPV values in schizophrenic patients. METHODS: This research method uses observational analytic design with a prospective cohort approach with pre and posts analysis conducted at the Regional Special Hospital of South Sulawesi Province during May - July 2018 in 30 schizophrenic subjects. The subjects were diagnosed as first episode schizophrenia based on ICD 10, blood samples were taken, for RDW and MPV values before and after haloperidol was given at the 4th and 8th weeks. RESULTS: The results showed that the mean RDW value at the 4th week was higher in 15 mg/day haloperidol group (15.8) compared to 7.5 mg/day haloperidol group (15.3) with p<0.05. Mean RDW value taken at 8th week was higher in 15 mg/day haloperidol group (16.4) compared to 7.5 mg/day haloperidol group (15.6) with p<0.001. Mean MPV value taken at 8th week was higher in 15 mg/day haloperidol group (13.3) compared to 7.5 mg/day haloperidol group (11.6) with p<0.001. CONCLUSION: This study showed an increase in the RDW value in schizophrenia subjects prior to the haloperidol administration. RDW and MPV values were higher after haloperidol treatment compares to before haloperidol treatment. The increase of RDW and MPV values tend to be influenced by haloperidol dosage and administration duration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".