An Integrated Data-Driven Model for Clinical Phenotyping of Tuberculosis Disease Severity
Bibliographic record
Abstract
A common approach to describing tuberculosis (TB) disease severity is to use a binary classification such as "advanced" and "minimal or early disease," though this may not fully capture the range of clinical presentations. As individuals transition through stages of disease, we expect to observe increased bacterial burden and inflammation which corresponds to worsening disease severity and increased risk of a negative outcome. We develop a new method, tuberculosis SeveriTy Assessment Tool for Informed Stratification (TB-STATIS), to understand the various disease severity phenotypes that exist at time of clinical presentation. Our method integrates data from multiple sources (i.e. smear microscopy, chest x-ray findings, symptoms, etc.) to identify a set of disease severity classes and obtain a predicted disease class for each individual given their observed data. Our approach is motivated by the statistical framework used in event-based modeling, a type of data-driven disease progression modeling. We show in simulation TB-STATIS can correctly identify the true set of disease classes with various sample sizes, data sources to integrate, and levels of uncertainty in the observed data. We apply TB-STATIS to two data sets, data from an observational TB cohort in South Africa and data from a global phase 3 clinical trial that tested the non-inferiority of two 4-month regimens compared to the standard 6-month regimen for the treatment of TB. We observe disease classes generated from TB-STATIS correlate with culture conversion, a proxy for TB treatment response. We demonstrate our approach to classifying TB disease severity generates clinically meaningful strata.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".