Blood screen findings in a 2-year cohort of newly arrived refugees to Sydney, Australia
Bibliographic record
Abstract
OBJECTIVES: To describe the prevalence of certain health conditions in newly arrived refugees to Sydney, Australia, and thereby help inform screening practices. STUDY TYPE: A clinical audit of routinely collected pathology results. METHODS: Demographics and pathology results from a nurse-led health assessment program for newly arrived refugees during 2013 and 2014 were analysed. Prevalences of screened conditions were calculated, and compared by country of birth and other demographic features. A specific category was created for those from Middle Eastern countries, for comparative analysis. RESULTS: Pathology results were analysed for 3307 people from 4768 seen by the assessment program (69.4%). Anaemia was found in 6% of males and 7.6% of females. Vitamin D deficiency (<50 nmol/L) was detected in 77.5%. Chronic hepatitis B was found in only 1.7% but in more than 10% of people from Burmese and Tibetan backgrounds. Strongyloides seropositivity was found in 4%. Among the subset tested for hepatitis C antibody, 0.5% were positive. No human immunodeficiency virus (HIV) infections were detected. More than 75% of the study population was from Middle Eastern countries. Compared with refugees from other regions, this subset had less anaemia (in females), more vitamin D deficiency, less chronic hepatitis B and less strongyloides seropositivity. CONCLUSIONS: People from refugee backgrounds have differing risks of conditions, based on demographics, migration history and prior screening. Postarrival testing should be tailored to each family and individual. Results of screening should be constantly reviewed and the approach updated based on findings. We support, in particular, the Canadian approach of only retesting HIV in refugees from countries with a high prevalence of infection (>1%).
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".