Experiences with latent TB cascade of care strengthening for household contacts in Calgary, Canada
Bibliographic record
Abstract
SETTING: Identification, assessment, and treatment of latent TB infection (LTBI), collectively known as the LTBI cascade of care, is critical for TB prevention. OBJECTIVE: The objective of this research, conducted within the ACT4 trial, was to assess and strengthen the LTBI cascade of care for household contacts at Calgary TB Services, a clinic serving a predominately foreign-born population in Western Canada. DESIGN: Baseline assessment consisted of a retrospective LTBI cascade analysis of 32 contact investigations, and questionnaires administered to patients and health care workers. Four solutions were implemented in response to identified gaps. Solution impact was measured for 6 months using descriptive statistics. RESULTS: Pre-implementation, 56% of household contacts initiated treatment. Most contacts were lost to care because the tuberculin skin test (TST) was not initiated, or physicians did not recommend treatment. Evening clinics, a patient education pamphlet, a nursing workshop, and treatment recommendation guidelines were implemented. Post-implementation, losses due to LTBI treatment non-recommendation were reduced; however, the overall proportion of household contacts initiating treatment did not increase. CONCLUSION: Close engagement between researchers and TB programmes can reduce losses in the LTBI cascade. To see sustained improvement in overall outcomes, long-term engagement and data collection for ongoing problem-solving are required.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".