Spectrum of Illness in International Migrants Seen at GeoSentinel Clinics in 1997–2009, Part 1: US-Bound Migrants Evaluated by Comprehensive Protocol-Based Health Assessment
Bibliographic record
Abstract
BACKGROUND: Many nations are struggling to develop structured systems and guidelines to optimize the health of new arrivals, but there is currently no international consensus about the best approach. METHODS: Data on 7792 migrants who crossed international borders for the purpose of resettlement and underwent a protocol-based health assessment were collected from the GeoSentinel Surveillance network. Demographic and health characteristics of a subgroup of these migrants seen at 2 US-based GeoSentinel clinics for protocol-based health assessments are described. RESULTS: There was significant variation over time in screened migrant populations and in their demographic characteristics. Significant diagnoses identified in all migrant groups included latent tuberculosis, found in 43% of migrants, eosinophilia in 15%, and hepatitis B infection in 6%. Variation by region occurred for select diagnoses such as parasitic infections. Notably absent were infectious tuberculosis, soil-transmitted helminths, and malaria. Although some conditions would be unfamiliar to clinicians in receiving countries, universal health problems such as dental caries, anemia, ophthalmologic conditions, and hypertension were found in 32%, 11%, 10%, and 5%, respectively, of screened migrants. CONCLUSIONS: Data from postarrival health assessments can inform clinicians about screening tests to perform in new immigrants and help communities prepare for health problems expected in specific migrant populations. These data support recommendations developed in some countries to screen all newly arriving migrants for some specific diseases (such as tuberculosis) and can be used to help in the process of developing additional screening recommendations that might be applied broadly or focused on specific at-risk populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".