Collect Once, Use Many Times: Attaining Unified Metrics for Tuberculosis Preventive Treatment for People Living With HIV
Bibliographic record
Abstract
The World Health Organization (WHO) recommends providing tuberculosis preventive treatment (TPT) to all persons living with HIV and to all household contacts of persons with bacteriologically confirmed pulmonary tuberculosis disease. Regrettably, the absence of a harmonized data collection and management approach to TPT indicators has contributed to programmatic challenges at local, national, and global levels. However, in April 2020, the WHO launched the Consolidated HIV Strategic Information Guidelines, with an updated set of priority indicators. These guidelines recommend that Ministries of Health collect, report, and use data on TPT completion in addition to TPT initiation. Both indicators are reflected in the WHO's list of 15 core indicators for program management and are also required by the US President's Emergency Plan for AIDS Relief's Monitoring, Evaluation, and Reporting (MER) guidance. Although not perfectly harmonized, both frameworks now share essential indicator characteristics. Aligned indicators are necessary for robust strategic and operational planning, resource allocation, and data communication. "Collect once, use many times" is a best practice for strategic information management. Building harmonized and sustainable health systems will enable countries to successfully maintain essential HIV, tuberculosis, and other health services while combatting new health threats.
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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 | 0.076 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".