Results from a roving, active case finding initiative to improve tuberculosis detection among older people in rural cambodia using the Xpert MTB/RIF assay and chest X-ray
Bibliographic record
Abstract
Cambodia has one of the highest tuberculosis (TB) prevalence rates in the world. People aged 55 years and over account for an estimated 50% of the country's TB burden, yet this group has a low notification rate owing to specific barriers in accessing health services. One-off active case finding (ACF) days with mobile GeneXpert and X-ray systems were organized at 75 government health facilities in four operational districts. Symptomatic community members with an abnormal chest X-ray were tested using the Xpert MTB/RIF assay. People with TB were then treated at health facilities after screening services moved onto the next site. De-identified project data were analysed to produce descriptive statistics about the people tested on Xpert and those diagnosed with TB. A linear regression was fit through the 12 quarters of National TB Program (NTP) TB case notification data immediately prior to ACF. The regression was used to calculate trend-expected notifications during and after the ACF quarters. Notifications from the ACF quarters were then compared to actual notifications from the previous year and to the trend-expected notifications during the ACF quarter by age group and type of TB. Finally, NTP TB treatment outcomes for the patients started on treatment during the ACF quarter were compared to those from a year prior. 2068 individuals submitted sputum for Xpert MTB/RIF testing, resulting in the identification of 319 (15.4%) bacteriologically-positive TB patients and an additional 574 people who were clinically diagnosed with TB. In the ACF quarters, new bacteriologically-positive notifications increased +119.2% for all ages and +262.7% for people aged 55 and over compared with trend-expected notifications. Treatment initiation figures remained above trend-expected notifications for three full quarters after ACF. The treatment success rate across all operational districts was significantly higher for patients detected in the ACF quarters (88.8% vs 94.5%, p = 0.012). A series of roving, one-off ACF days at government health facilities were able to increase TB diagnosis, treatment initiation and treatment outcomes in a key population with high TB prevalence. Targeted ACF interventions such as this could be used to reduce a backlog of untreated, prevalent TB.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".