High Risk for Tuberculosis in Hospital Physicians, Peru
Bibliographic record
Abstract
To the Editor: Occupational exposure to Mycobacterium tuberculosis poses a major risk to medical staff worldwide.In areas of high tuberculosis (TB) incidence such as Peru (228-364 cases/100,000 [1,2]), the risk for hospital workers may be 40 times greater than that for the general population (3).Prospective studies to evaluate the precise occupational risk to medical staff in developing countries with a high incidence of TB disease are lacking.We evaluated the incidence of TB skin test (TST) conversion and TB disease in interns and residents in a teaching hospital in Lima, Peru.Our study included 54 medical interns and 45 residents who began training in April 2000 at the Carrión Hospital, where all patients with TB are housed in wards without respiratory isolation.Each study participant had an initial evaluation before beginning hospital training.TST and chest radiographs were performed on entry into the study and 11 months later.TSTs were performed by the standard Mantoux technique, that is, intradermal injection of purified protein derivative (5 tuberculin units/0.1 mL) (Connaught Ltd., Ontario, Canada).Indurations were measured 48-72 hours later.A positive result was defined as an induration >10 mm.TST conversion from a negative to a positive result was defined as an increase of >10 mm in induration, according to criteria of the Centers for Disease Control and Prevention (CDC) (4).Every 3 months, physicians were screened for signs and symptoms of TB, and at 9 months they were interviewed for risk factors such as area of service, contact with active TB cases, and usage of an approved mask (N95 type).All statistical analyses were performed by using STATA 7.0 (Stata Corporation, College Station, TX).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".