Resistance patterns of Mycobacterium tuberculosis isolates from pulmonary tuberculosis patients in Nairobi
Bibliographic record
Abstract
INTRODUCTION: In Kenya, which ranks thirteenth of 27 high tuberculosis burden countries, diagnosis is based on Ziehl-Neelsen staining alone and patients are treated without information on sensitivity patterns. This study aimed to determine resistance patterns of Mycobacterium tuberculosis isolated from pulmonary samples. METHODOLOGY: Pulmonary tuberculosis patients in Nairobi were randomly sampled after informed consent and recruited into the study using a structured questionnaire. Specimens were cultured in liquid and solid media, and drug susceptibility tests were performed for first-line drugs including (isoniazid, rifampin, streptomycin, ethambutol and pyrazinamide). RESULTS: Eighty-six (30%) of 286 isolates were resistant to at least one of five antibiotics tested. Thirty-seven (30.2%) isolates were resistant to isoniazid; 15 (11.6%) to streptomycin; 13 (4.5%) to ethambutol; four (1.4%) to rifampin ; and 30 (10.4%) to pyrazinamide. Double resistance was seen as follows: four (1.4%) isolates were resistant to both isoniazid and pyrazinamide; four (1.4%) to streptomycin and isoniazid; and one (0.3%) to rifampin and streptomycin. Two isolates (0.7%) were multidrug resistant, and one was triple resistant with an additional resistance to ethambutol. Results also showed 88.7% of patients were below the age of 40 years, while 26.3% were HIV positive. The majority of the patients (66.5%) were unemployed or self-employed in small businesses, with 79.4% earning less than 100 USD per month. CONCLUSION: The high resistance observed in isoniazid, which is a first-line drug, could result in an increase in multidrug resistance unless control programs are strengthened. Poverty should be addressed to reduce infection rates.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".