Type and physical intensity of occupations at pulmonary TB diagnosis
Bibliographic record
Abstract
BACKGROUND: Pulmonary TB (PTB) predominantly affects individuals of working age. We sought to characterise the occupations of people newly diagnosed with PTB in Karachi, Pakistan, by type and physical intensity. DESIGN/METHODS: We did a secondary analysis of data from a study evaluating the diagnostic accuracy of artificial intelligence-based chest X-ray (CXR) analysis software, where individuals had been evaluated for active PTB using sputum cultures and had provided information on occupation. We used an accelerometer-validated US National Health and Nutrition Examination Survey-based job categorisation to assign physical activity levels to participant-reported occupations as High, Intermediate, or Low. RESULTS: Among 272 participants with microbiologically confirmed PTB (women: 130/272, 48%; median age: 29 years, IQR 22-45), 78% (211/272) had smear-positive disease, and 96% (260/272) had data on occupation. Unemployment was common (women: 70/122, 57%; men: 23/138, 17%). Most women reporting an occupation were homemakers (21/52, 40%), and 54% (28/52) had an intermediate- or a high physical activity occupation. Among men reporting an occupation, 35% (40/115) were labourers, and 79% (91/115) had an intermediate- or high-physical activity occupation. CONCLUSION: The majority of individuals with PTB were in their working age, had extensive disease, and had intermediate or high physical activity occupations, suggesting economic vulnerability due to physical impairment.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".