Analysis of SGOT, SGPT, and IgM anti PGL-1 in Multibacillary Leprosy Patient after Multi Drug Therapy
Bibliographic record
Abstract
INTRODUCTION: Leprosy is a fairly dreaded disease, but it is curable. However, liver failure is one of the side effect of the treatment that challenging to manage.OBJECTIVES: Assessing the effects of Multi Drug Therapy (MDT) on the liver function (SGOT, SGPT) and Mycobacterium Leprae Particle Agglutination (MLPA) test (IgM anti PGL-1) before and after treatment in patients with multibacillary (MB) leprosy.METHOD: Twenty-eight patients who met the inclusion criteria were enrolled in this study which categorized as new MB leprosy patients in Dr. Wahidin Sudirohusodo Hospital, Makassar, Indonesia. In order to test the liver function, blood serum was taken to measure the SGOT and SGPT level with Bochringer Mannheim automatic analysis, while MLPA test measurement was performed with qualitative method. Blood serum was collected three times with the following period; before the treatment, 3 months, and 6 months after treatment. The data was analyzed using Friedman and Wilcoxon Signed Rank test with significant level p<0.001.RESULT: There were significant increases in SGOT and SGPT levels (p<0.001) before and after MDT treatment between 3 to 6 months. Meanwhile, for IgM anti PGL-1, it was not significant (p>0.01) before treatment and after 3 months treatment, but significant different (p<0.001) on 6 months treatment.CONCLUSION: MDT treatment on MB leprosy patient increase the SGOT and SGPT level but decrease the IgM anti PGL-1 after the 6 months of treatment.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".