Predicting transmission of tuberculosis from patient attributes
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".