Predicting pulmonary tuberculosis in immigrants: a retrospective cohort study
Bibliographic record
Abstract
Our objective was to investigate whether pulmonary tuberculosis (PTB) can be predicted from features of a targeted medical history and basic laboratory investigations in immigrants. A retrospective cohort of 391 foreign-born adults referred to the Edmonton Tuberculosis Clinic (Edmonton, AB, Canada) was studied using multiple logistic regression analysis to predict PTB. Seven characteristics of disease were used as explanatory variables. Cross-validation assessed performance. Each predictor was tested on two outcomes: “culture-positive” and “smear-positive”. Receiver operating characteristic (ROC) curves were generated and the area under the ROC curve (AUC) was quantified. Symptoms, subacute duration of symptoms, risk factors for reactivation of latent TB infection and anaemia were all associated with a positive culture (adjusted OR 1.79, 2.24, 1.72 and 2.28, respectively; p<0.05). Symptoms, inappropriate prescription of broad-spectrum antibiotics and a “typical” chest radiograph were associated with smear-positive PTB (adjusted OR 2.91, 1.55 and 12.34, respectively; p<0.05). ROC curve analysis was used to test each model, yielding AUC=0.91 for the outcome “culture-positive” disease and AUC=0.94 for the outcome “smear-positive” disease. PTB among the foreign-born can be predicted from a targeted medical history and basic laboratory investigations, raising the threshold of suspicion in settings where the disease is relatively rare.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".