Latent tuberculosis infection (LTBI) treatment completion and complication in Leeds, UK
Bibliographic record
Abstract
Background: Successful LTBI treatment (Rx) is essential in controlling TB in low incidence countries. Reported LTBI Rx completion rate in USA, Canada and UK is around 50% [1.2]. In Leeds we run an active screening programme for both new entrant and contact screening with a great emphasis on patient education and close follow-up. Methods: We conducted a retrospective review of all the LTBI patients (excluding those starting Rx pre anti-TNF) who were offered and accepted Rx in 2009 in Leeds. We looked at the completion rate, side effects (SE) of the Rx and reasons for not completing Rx. Results: 184 LTBI patients were offered and accepted Rx. 89% (163/184) of the patients successfully completed the Rx. Table 1 Patient number Percentage Total number of patients 184 Male 96 52% Female 88 48% Age Range 1 month–48 years Mean 25.7 years Median 28 years Source of referral New entrant screening 136 74% Contact tracing 36 20% Occupational health referral 12 6% Treatment Isoniazid 6 months 28 15% Isoniazid + Rifampicin 3 months 155 84% Rifampicin 6 months 1 Treatment completed (attended last schedule appointment) 163 89% Of those not completing the Rx 38% (8/21) had moved out of Leeds and another 38% (8/21) failed to follow up without known reason, only 3 patients stopped Rx due to SE. Conclusion: Good compliance is achievable with patient education and close follow-up. Treatment side effects of LTBI are usually mild and self resolving. References: 1 Horsburgh C.R. Jr et al. Latent TB infection treatment acceptance and completion in the United States and Canada. Chest 2010;137(2):401-9. 2 Rennie T.W. et al. Patient choice promotes adherence in preventive treatment for latent tuberculosis. Eur Respir J 2007;30:728-735.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".