Loss to follow-up among adults with drug-resistant TB in Papua New Guinea
Bibliographic record
Abstract
SETTING: Multidrug-resistant/rifampicin-resistant TB (MDR/RR-TB) is now endemic in the National Capital District (NCD), Papua New Guinea. Loss to follow-up (LTFU) is a challenge. OBJECTIVE: To evaluate and identify risk factors for LTFU, including pre-treatment LTFU, in adults with MDR/RR-TB at Port Moresby General Hospital (PMGH). DESIGN: A retrospective analysis of treatment initiation in adults diagnosed with MDR/RR-TB (2018-2022) and outcomes for a cohort treated for MDR/RR-TB (2014-2019). We assessed the factors associated with LTFU using multivariate logistic regression. RESULTS: Of 95 patients diagnosed with MDR/RR-TB at PMGH from 2018 to 2022, 21 (22%) were lost to follow-up before treatment. Of the 658 adults who initiated treatment for MDR/RR-TB at PMGH from 2014 to 2019, 161 (24%) were lost to follow-up during treatment. A higher proportion of patients on injectable-containing long regimens (110/404, 27%) were lost to follow-up than those on the all-oral regimen containing bedaquiline (13/66, 12%). Treatment loss to follow-up was associated with age (35-54 years age group: aOR 0.49, 95% CI 0.32-0.77; 55-75 years age group: aOR 0.42, 95% CI 0.19-0.90; compared to the 15-34 years age group), residence outside of NCD (aOR 1.79, 95% CI 1.04-3.06), and year of treatment initiation. CONCLUSION: Pre-treatment LTFU requires programmatic focus. Shorter oral regimens and decentralised services may address the reasons for higher LTFU in younger people and people living outside NCD.
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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 | 0.006 |
| 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 | 0.000 |
| 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".