Predicting transmission of tuberculosis from patient attributes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: A newly diagnosed tuberculosis (TB) case can be classified as: 1) a source case for transmission leading to other, secondary active TB cases; 2) a secondary case, resulting from recent transmission; or 3) an isolated case, uninvolved in recent transmission (i.e. neither source nor recipient). Accurate classification of newly diagnosed patients should help public health personnel to direct TB control activities. Objective: To aid prevention of TB transmission through effective management of newly diagnosed TB cases, a multinomial logistic regression model was developed to estimate the probability of a new case being one of three classes (i.e., source, secondary, isolated) based on the case's clinical and socio-demographic data, such as age, HIV status, and chest X-ray result. Methods: Attributes of TB cases reported on the island of Montreal between 1996 and 2007 were multiply imputed and used to fit the model. DNA fingerprint analysis was used as the reference standard to define the dependent variable of the model. Variable selection was performed by Bayesian Model Averaging, and 10 repeats of 10-fold cross-validation were performed on each of the imputed datasets to measure the predictive performance of the model using the Area Under the Receiver Operating Curve (AUC). Results: A total of 1552 cases, comprised of 107(6.9%) source cases, 207(13.4%) secondary cases, and 1238 (79.8%) isolated cases, were available to develop the model. AUC of the model to discriminate source, secondary, and isolated case was 0.59 (95% CI: 0.54, 0.65), 0.64 (95% CI: 0.62, 0.67), and 0.65 (95% CI: 0.63, 0.67), respectively. Conclusion: The performance of the prediction model was significantly better than random prediction. Further study is needed to assess its ability to improve TB control in public health practice.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it