Identifying gaps in the quality of latent tuberculosis infection care
Bibliographic record
Abstract
Latent tuberculosis infection (LTBI) occurs after transmission and acquisition of infection, when the tuberculosis (TB) bacteria lie dormant in a person. Nearly one-quarter of the world's population is estimated to have LTBI, yet few studies have been published assessing the quality of LTBI services globally. This paper reviews issues to providing patient-centered LTBI services and offers an example framework to formally assess the quality of LTBI patient care. By applying the LTBI cascade of care model, TB programmes can evaluate the gaps and barriers to high-quality care and develop locally-driven solutions to improve LTBI services. Quality care for LTBI must address some of the key challenges to services including: (1) low prioritization of LTBI; (2) gaps in healthcare provider knowledge about testing and treatment; and (3) patient concerns about side effects of preventive treatment regimens. TB programmes need to ensure that these issues are addressed in a patient-centered manner, with clear communication and ongoing evaluation of the quality of LTBI services. Quality LTBI care must be a central focus, particularly identifying and engaging more household contacts in preventive treatment, in order to halt the progression to active disease thereby stopping TB transmission globally.
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 | 0.073 | 0.205 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".