Patterns of drug resistance in Mycobacterium tuberculosis from tuberculosis patients in Ibadan Nigeria
Bibliographic record
Abstract
The success of the global tuberculosis (TB) control program has been threatened with drug resistant strains emergence; especially the Multidrug Resistant Tuberculosis (MDR-TB). Despite that Nigeria is one of the countries with high tuberculosis burden, little is known on the magnitude of MDR-TB in the country. This study was to determine drug resistant patterns of Mycobacterium tuberculosis isolated from patients that attended Directly Observed Treatment Short-course (DOTS) centres in Ibadan, Nigeria. Sputum samples collected from confirmed TB patients were processed using the N-acetyl L-cysteine-sodium hydroxide decontamination method. Direct drug susceptibility test was carried out against rifampicin, isoniazid, ethambuthol and streptomycin. Out of the 319 samples collected, 149 (46.7%) were culture positive and susceptibility test was completed for 101 (67.8%) isolates, out of which any resistance and mono-resistance to rifampicin was 23.8% and 8.9% respectively. In all 11.9% MDR-TB was observed comprising 30.8% (acquired), and 8.3% (primary) while, 3.96% showed resistance to all tested drugs. The patterns of MDR-TB in this study indicates that active case findings as well as expansion of drug susceptibility testing is required to effectively control TB, drug resistant strains and to forestall the transmission and spread of the drug-resistant TB in the society.
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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 | 0.001 | 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 | 0.000 |
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".