When students become patients: TB disease among medical undergraduates in Cape Town, South Africa
Bibliographic record
Abstract
BACKGROUND: Medical students acquire latent tuberculosis (TB) infection at a rate of 23 cases/100 person-years. The frequency and impact of occupational TB disease in this population are unknown. METHODS: A self-administered questionnaire was distributed via email and social media to current medical students and recently graduated doctors (2010 - 2015) at two medical schools in Cape Town. Individuals who had developed TB disease as undergraduate students were eligible to participate. Quantitative and qualitative data collected from the questionnaire and semi-structured interviews were analysed with descriptive statistics and a framework approach to identify emerging themes. RESULTS: Twelve individuals (10 female) reported a diagnosis of TB: pulmonary TB (n=6), pleural TB (n=3), TB lymphadenitis (n=2) and TB spine (n=1); 2/12 (17%) had drug-resistant disease (DR-TB). Mean diagnostic delay post consultation was 8.1 weeks, with only 42% of initial diagnoses being correct. Most consulted private healthcare providers (general practitioners (n=7); pulmonologists (n=4)), and nine underwent invasive procedures (bronchoscopy, pleural fluid aspiration and tissue biopsy). Substantial healthcare costs were incurred (mean ZAR25 000 for drug-sensitive TB, up to ZAR104 000 for DR-TB). Students struggled to obtain treatment, incurred high transport costs and missed academic time. Students with DR-TB interrupted their studies and experienced severe side-effects (hepatotoxicity, depression and permanent ototoxicity). Most participants cited poor TB infection-control practices at their training hospitals as a major risk factor for occupational TB. CONCLUSIONS: Undergraduate medical students in Cape Town are at high risk of occupationally acquired TB, with an unmet need for comprehensive occupational health services and support.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".