Using a quality improvement approach to improve care for latent tuberculosis infection
Bibliographic record
Abstract
INTRODUCTION: Latent tuberculosis infection (LTBI) management is recognized as a key component of the World Health Organization End Tuberculosis Strategy. The term 'cascade of care in LTBI' has recently been used to refer to the process of LTBI management from identification of persons who may have LTBI to completion of treatment. Large gaps throughout the LTBI cascade of care have been identified. Areas covered: We have reviewed quality improvement (QI) as a potential approach for systematically improving gaps within the LTBI cascade of care. QI principles and approaches were reviewed, as well as the determinants of losses and evidence for solutions (interventions) within the LTBI cascade of care. An example of QI application in LTBI management is described. Expert commentary: Improving LTBI care at the magnitude required to reach the End TB Strategy goals will require systematic and context specific improvements at all steps in the cascade of care in LTBI. A continuous QI approach based on systems thinking, use of locally gathered data, and an iterative learning process can facilitate the process required to make the necessary improvements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".