Improving tuberculosis diagnosis: Better tests or better healthcare?
Bibliographic record
Abstract
Tuberculosis (TB) is a preventable and curable disease, but it kills more people than any other infection.Many people with TB are never diagnosed, and those who are diagnosed are often ill and contagious for many weeks or months before a diagnosis is made.Barriers to TB diagnosis are well described, often including poverty; stigma; marginalization; indolent, nonspecific symptoms; and poorly performing diagnostic tests.However, despite their central role in TB diagnosis, healthcare providers have been the subject of surprisingly little research [1].This week in PLOS Medicine, Sylvia and colleagues report findings with important implications for TB elimination [1].They trained and sent simulated "standardized patients," also known as "mystery clients," to healthcare providers at village clinics, township health centers, and county hospitals in China and found that the care provided in 274 consultations differed greatly from TB recommendations.The standardized patients reported classical TB symptoms, but only 15% of the providers mentioned TB, and only 41% of the providers tested or referred patients as recommended for TB.These differences between policy and practice were especially marked in the village clinics where most care was provided, and simulations suggested that a proposed system of managed referral with gatekeeping at the level of the village clinic would further reduce correct management, all of which makes for uncomfortable reading. Tuberculosis testing policy-practice gapTB policies generally recommend sputum testing for diagnosing pulmonary TB [4], whereas in this study, X-rays were more popular.This policy-practice gap is more complex than a shortfall in practice, partly because sputum TB testing is more likely to be stigmatized and is
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".