How much does the plain chest radiograph underestimate the extent of disease in subclinical pulmonary tuberculosis?
Bibliographic record
Abstract
Abstract Subclinical pulmonary tuberculosis (PTB) is a recently described intermediate state of great interest, but about which little is known. This study sought to estimate the extent to which chest radiographs (CXRs), as compared to computed tomographic (CT) scans, under-detect key radiologic features in subclinical PTB, and to interpret the clinical and public health relevance of the differences. Diagnostic CXRs and CT scans of the thorax and neck in a 16-year cohort of subclinical PTB patients in Canada were re-acquired and read by two independent readers and arbitrated by a third reader. Logistic regression models were fit to determine how likely CXR features can be detected by CT scan versus CXR after adjustment for age and sex. Among 296 subclinical patients, CXRs were available in 286 (96.6%) and CT scans in 94 (32.9%). CXR features in patients with and without CT scans were comparable. Lung cavitation was 4.77 times (95% CI, 1.95–11.66), endobronchial spread 19.36 times (95% CI, 8.05–46.52), and moderate/far-advanced parenchymal disease 3.23 times (95% CI, 1.66–6.30), more common on CT scan than CXR. We conclude that the extent to which CXRs under-detect key radiologic features in subclinical PTB is substantial. This may have public health and treatment implications.
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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 | 0.016 | 0.081 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".