The Presence of Cough and Tuberculosis: Active Case Finding Outcomes in the Philippines
Bibliographic record
Abstract
The results of a tuberculosis (TB) active case finding (ACF) program, implemented by International Care Ministries (ICM) in the Philippines, were examined to understand how the presence of physical symptoms might influence ACF outcomes among extreme low-income Filipinos. ICM health staff implemented symptom screening in villages and suspected cases were referred to the closest rural health unit (RHU) for TB testing. ACF was carried out in Mindanao and the Visayas, across 16 different provinces. All participants were interviewed pre/postprogram, and screening outcomes were collected. A multilevel regression model was constructed to explore the effect of factors on the likelihood of getting tested. A total of 4635 individuals were screened; 1290 (27.8%) were symptom positive and referred. From those referred, 336 (7.2%) were tested for TB and 53 (1.1%) were TB positive. "Cough for more than two weeks" was associated with a 1.09 (95% CI 1.01, 1.15) times increase in likelihood of getting tested. The finding that the presence of cough is associated with higher rate of testing suggests that individuals in these settings might not know or believe that the lack of cough does not equate to lack of TB infection. While technologies and screening algorithms give us the ability to refine the 'supply' side of the TB screening, addressing the knowledge gap should improve 'demand'.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".