Age- and sex-specific care cascades to detect gaps in the care of children with tuberculosis in Bangladesh: a cohort study
Bibliographic record
Abstract
Background: Programmatic interventions to increase the detection of children with tuberculosis (TB) are rarely evaluated to understand age- and sex-specific completion rates. We applied modified TB screening and treatment cascade frameworks to assess indicators of effective implementation by age and sex of a TB screening program for children (zero to 14 years) in Bangladesh. Methods: tests. Results: In total, we screened 552 182 males and 461 419 females for TB. 2.8% of males and 2.6% of females screened positive (P < 0.001). 74.2% of males and 73.9% of females underwent appropriate evaluation (P = 0.560). 10.3% of males and 11.5% of females were diagnosed with TB (P = 0.008). 100% of children initiated treatment, and 97.6% of males and 97.1% of females achieved a successful treatment outcome (P = 0.428). The percent of children screening positive on verbal screen, who were clinically evaluated for TB, and who were diagnosed with TB generally increased with age, with some variability throughout (ranges: 1.2-9.1%, 59.8-88.5%, 6.5-21.9%, respectively). Conclusions: The largest gap observed for both sexes and among all ages was children who were not appropriately evaluated for TB despite screening positive. In our research, we highlight the value of identifying gaps in paediatric TB care to inform innovative, age- and sex-tailored interventions to improve future care in children.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".