Additional file 1 of Resource implications of the latent tuberculosis cascade of care: a time and motion study in five countries
Bibliographic record
Abstract
Additional file 1: Supplemental Table 1. Predicted health care personnel time to perform all tasks in the LTBI Cascade of Care for all household contacts (HHC) of one index patient in Benin (observed data: 3.9 HHC per index patient). Supplemental Table 2. Predicted health care personnel time required for HCW to perform all tasks in the LTBI Cascade of Care for all household contacts (HHC) of one index patient in Canada (observed data: 3.6 HHC per index patient). Supplemental Table 3. Predicted health care personnel time required for HCW to perform all tasks in the LTBI Cascade of Care for all household contacts (HHC) of one index patient in Ghana (observed data: 8.8 HHC per index patient). Supplemental Table 4. Predicted health care personnel time required for HCW to perform all tasks in the LTBI Cascade of Care for all household contacts (HHC) of one index patient in Indonesia (observed data: 3.3 HHC per index patient). Supplemental Table 5. Predicted health care personnel time required for HCW to perform all tasks in the LTBI Cascade of Care for all household contacts (HHC) of one index patient in Vietnam (observed data: 2.8 HHC per index patient).
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.681 | 0.060 |
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".