Tuberculosis among Health Care Workers
Bibliographic record
Abstract
The objective of this work was to address the problem of tuberculosis (TB) infection among Health Care Workers (HCW), based on an experience in a TB Prevention and Treatment Programme at a General Acute Hospital in Buenos Aires City. The objectives of the programme were to provide health education and periodic screening of HCW, to detect TB cases and to implement control measures. TB infection was defined by a 10 mm reaction to 2 UT PPD. Two prevalence studies of TB infection were performed. In 1998 infection prevalence was 31.7% (n = 533), and in 2008 it was 35% (n = 670). Nurses presented the highest percentage. An increase in TB infection prevalence was observed in 2008 among physicians (p = 0.02). A sample of 45 HCW integrating the first prevalence study group was analysed by applying 2 UT PPD three month later. Eight tuberculin conversions were detected (17%), all of them in nurses; 163 HCW who had contact with other eight HCW who developed TB were evaluated, four of whom were nurses. PPD reactions were positive in 39.9% with no any new case of TB detected among them. In nurses, five tuberculin conversions were observed. Other 127 HCW from low risk areas were also evaluated, 25.9% of whom had positive reactions to 2 UT PPD, while this percentage was 39.9% in the previous group (p = 0.01).Despite the scarcity of human resources that limit the planning of TB incidence studies, personal protective measures, teaching HCW about TB and chemoprophylaxis might reduce the risk of TB in this group.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".