Hepatitis C outbreak in a respiratory care ward associated with frequent injections: Taiwan, 2017
Bibliographic record
Abstract
BACKGROUND: Healthcare-associated outbreaks of hepatitis C virus (HCV) infection pose serious risks of harm to patients. During May-July 2017, the Taiwan Centers for Disease Control were notified of four patients with acute HCV infection in a respiratory care ward (RCW). To prevent further infection, an investigation was conducted to identify the transmission route and risk factors for infection. METHODS: We tested patients and staff members of the RCW for HCV, reviewed medical records, observed infection control practices on-site, and undertook a case-control study. We defined cases as individuals who had stayed in the RCW 2 weeks to 6 months prior to the laboratory diagnosis date of the first case and were infected with HCV after admission. Patients who were hospitalized during the same period but whose HCV tests were negative were selected as controls. We used Mann-Whitney U test to compare the frequency of injections among cases and controls. RESULTS: Of 19 staff and 29 patients, we identified four case-patients and one patient with chronic hepatitis C whose HCV RNA similarity was >98%. Compared to the 12 controls, the case-patients received more injections per day (4.4 vs. 0.1; p = 0.01). The RCW lacked designated areas and standardized workflows for injection preparation. Disinfection of the environment and equipment was inadequate, which could possibly lead to blood contamination of the environment and parenteral medications. CONCLUSION: HCV infection was associated with frequent injections and infection control lapses. Healthcare workers should follow safe injection practices and reduce injection frequency to prevent HCV transmission.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".