Age-specific effectiveness of a tuberculosis screening intervention in children
Bibliographic record
Abstract
OBJECTIVE: To apply a cascade-of-care framework to evaluate the effectiveness-by age of the child-of an intensified tuberculosis patient-finding intervention. DESIGN: From a prospective screening program at four hospitals in Pakistan (2014-2016) we constructed a care cascade comprising six steps: screened, positive screen, evaluated, diagnosed, started treatment, and successful outcome. We evaluated the cascade by each year of age from 0 to 14 and report the age-specific mean proportion and standard deviation. RESULTS: On average across all ages, only 12.5% (standard deviation: 2.0%) of children with a positive screen were not evaluated. Among children who had a complete evaluation, the highest percentages of children diagnosed with tuberculosis were observed in children 0-4 (mean: 31.9%; standard deviation: 4.8%), followed by lower percentages in children 5-9 (mean: 22.4%; standard deviation: 2.2%), and 10-14 (mean: 26.0%; standard deviation:5.4%). Nearly all children diagnosed with tuberculosis initiated treatment, and an average of 93.3% (standard deviation: 3.3%) across all ages had successful treatment outcomes. CONCLUSIONS: This intervention was highly effective across ages 0-14 years. Our study illustrates the utility of applying operational analyses of age-stratified cascades to identify age-specific gaps in pediatric tuberculosis care that can guide future, novel interventions to close these gaps.
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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".