Tuberculosis among nomads in Adamawa, Nigeria: outcomes from two years of active case finding
Bibliographic record
Abstract
BACKGROUND: Nomadic populations are often isolated and have difficulty accessing health care, leading to increased morbidity and mortality. Although Nigeria has one of the highest tuberculosis (TB) burdens in Africa, case detection rates remain relatively low. METHODS: Active case finding for TB among nomadic populations was implemented over a 2-year period in Adamawa State. A total of 378 community screening days were organised with local leaders; community volunteers provided treatment support. Xpert(®) MTB/RIF was available for nomads with negative smear results. RESULTS: Through active case finding, 96 376 nomads were verbally screened, yielding 1310 bacteriologically positive patients. The number of patients submitting sputum for smear microscopy statewide increased by 112% compared with the 2 years before the intervention. New smear-positive notifications increased by 49.5%, while notifications of all forms of TB increased by 24.5% compared with expected notifications based on historical trends. Nomads accounted for respectively 31.4% and 26.0% of all smear-positive and all forms TB notifications. Pre-treatment loss to follow-up and treatment outcomes were similar among nomads and non-nomads. DISCUSSION: Nomads in Nigeria have high TB rates, and active case-finding approaches may be useful in identifying and successfully treating them. Large-scale interventions in vulnerable populations can improve TB case detection.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".