Chest radiograph abnormalities associated with tuberculosis: reproducibility and yield of active cases.
Bibliographic record
Abstract
SETTING: Tertiary care referral centre specialising in respiratory diseases. OBJECTIVES: Chest radiography is a major screening and diagnostic tool for tuberculosis (TB). We evaluated the reproducibility of a radiographic classification system for screening for active TB of immigration applicants to Canada. We also evaluated the validity of this classification system for detection of prevalent active TB among the screened applicants, as well as tuberculin-positive close contacts and symptomatic patients. METHODS: Reproducibility was assessed by re-reading a randomly selected 10% sample of screening chest films. Validity was estimated from the final clinical and microbiologic diagnosis of patients undergoing detailed clinical evaluation. RESULTS: Inter-reader agreement using five broad categories was moderate (kappas of 0.44-0.56), while intra-reader agreement was substantial (kappas of 0.59-0.72). After adjustment for age and patient group, the adjusted odds of active tuberculosis, relative to normal or minor findings or granulomas, for fibronodular changes was 10.2 (95% confidence interval [CI] 3.2-33), for mass or pleural effusion it was 11.6 (95%CI 3.6-37), and for parenchymal infiltrate it was 46.1 (95%CI 18-117). Among tuberculin-positive close contacts, the probability of active tuberculosis was more than 50% if the radiographs showed any mass, pleural disease, or parenchymal infiltrates. CONCLUSION: A simple classification of TB-related chest radiographic abnormalities into five broad categories had moderate to substantial reproducibility of readings, with reasonable validity.
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 | 0.019 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".