Profile of Drug-Resistant Tuberculosis Patients at Rumah Sakit Universitas Indonesia Depok Period October 2024
Bibliographic record
Abstract
Indonesia has been one of the three countries in Asia that contributes to the highest case of Tuberculosis (TB) in the world. Tuberculosis itself counted as the top ten cause of death globally. Two of the challenges in controlling TB cases are the occurrence of drug-resistance strain and patients’ adherence. Drug-resistant TB has to be treated with the second line drugs of TB with higher risk of adverse events. Linezolid as one of the suggested drugs by WHO in the treatment may increase patients’ risk of cytopenia events. This study was conducted to represent the profile of drug-resistant TB obtained from Rumah Sakit Universitas Indonesia (RSUI) Depok period October 2024. Design of this study is a quantitative-descriptive where the data of patients obtained from the patients’ medical records in the period of October 2024. The findings of this study showed the total number of drug-resistant TB was 104 with 3 cases of HIV and 70 patients having Linezolid in their regimen. Most of the patients are male with mean of age 41.3 years. New cases and RR/MDR TB level are dominated while on the other hand there’s 9 cases of After Failure and 4 others are After Loss to Follow-up. Median values obtained for patients’ leukocytes, hemoglobin, and platelets are 7818.0, 11.9, 279.5 respectively.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".