Additional file 1 of Bayesian latent class analysis produced diagnostic accuracy estimates that were more interpretable than composite reference standards for extrapulmonary tuberculosis tests
Bibliographic record
Abstract
Additional file 1: Supplemental Figure S1. Pairwise residual correlation plots for tests run on A) lymph node, B) CSF, and C) pleural fluid. Test pair 1: culture and Xpert; 2: culture and smear microscopy; 3: culture and cytopathology/histopathology or ADA; 4: Xpert and smear microscopy; 5: Xpert and cytopathology/histopathology or ADA; 6: smear microscopy and cytopathology/histopathology or ADA. ADA – adenosine deaminase; CSF – cerebral spinal fluid; TB – tuberculosis. Supplemental Figure S2. Density plots. For each of the three models (one per form of extrapulmonary TB), density plots were generated for each parameter of interest. For test result parameters specificity, C, and sensitivity, S, the indices 1, …,4 indicate culture, Xpert, smear microscopy, and ADA or cytopathology/histopathology. Supplementary Table S1. Probabilities of the four latent classes, for each form of extrapulmonary TB as estimated by Bayesian latent class analysis. Supplementary Table S2. Diagnostic accuracies of tests for each form of extrapulmonary TB by latent class analysis. Supplementary Table S3. Prevalence of each form of extrapulmonary TB as estimated by the two-class latent class model
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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 | 0.008 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.795 | 0.141 |
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".