Closing the loop in child TB contact management: completion of TB preventive therapy outcomes in western Kenya
Bibliographic record
Abstract
SETTING: Children especially those <5 years of age exposed to pulmonary tuberculosis (TB) are at a high risk of severe TB disease and death. Isoniazid preventive therapy (IPT) has been shown to decrease disease progression by up to 90%. Kenya, a high TB burden country experiences numerous operational challenges that limit implementation of TB preventive services. IPT completion in child contacts is not routinely reported in Kenya. OBJECTIVE: This study aims to review the child contact management (CCM) cascade and present IPT outcomes across 10 clinics in western Kenya. DESIGN: A retrospective chart review of programmatic data of a TB Reach-funded active, clinic-based CCM strategy. RESULTS: Of 553 child contacts screened, 231 (42%) were reported symptomatic. 74 (13%) of the child contacts were diagnosed with active TB disease. Of those eligible for IPT, 427 (90%) initiated IPT according to TB REACH project data while 249 (58%) were recorded in the IPT register with 49 (11%) recorded as a transfer to other facilities. Of the 249 recorded in the IPT register, 205 (82%) were documented to complete therapy (48% of project initiation children). CONCLUSION: Our evaluation shows gaps in the routine CCM care cascade related to completeness of documentation that require further programmatic monitoring and evaluation to improve CCM outcomes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".