Additional file 1 of Isoniazid or rifampicin preventive therapy with and without screening for subclinical TB: a modeling analysis
Bibliographic record
Abstract
Additional file 1: Supplemental methods, Tables S1-S6, and Figures S1-S5. Supplemental methods – Additional details of primary human subjects data collection, estimation of model parameters, state-transition model, and approach to probabilistic parameter sampling. Table S1. Parameter estimates, sizes of latent and subclinical progressor populations. Table S2. Parameter estimates, preventive therapy efficacy and related parameters. Table S3. Parameter estimates, preventive therapy resistance acquisition. Table S4. Parameter estimates, TB treatment outcomes. Table S5. Composition of cohorts with respect to initial drug resistance and age or CD4 count. Table S6. Correspondence between baseline culture-positive TB prevalence and 3-month clinical TB incidence in cohort of people with newly diagnosed HIV and a negative TB symptom screen in Kwa-Zulu Natal. Fig S1. – Projected outcomes of 4R or 6H, each compared to no TPT, among a cohort of 1000 all-age Household contacts. Fig S2. Head-to-head comparison of TB outcomes after TPT, comparing 4R regimen to 6H. Fig S3. Effect of reduced access on the impact of subclinical TB screening prior to TPT, among HHC cohort. Fig S4. Sensitivity of key results to individual parameters. Fig S5 – Projected outcomes of 4R or 6H, each compared to no TPT, among 1000 household contacts, when TPT is only considered for the 17% of contacts who are under age 5
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.744 | 0.089 |
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".