Migrants rescued on the Mediterranean Sea route: nutritional, psychological status and infectious disease control
Bibliographic record
Abstract
INTRODUCTION: North Africa has become a key migratory hub where a large number of migrants attempt the journey by sea from the Libyan coastline to the south of Europe. In this humanitarian disaster scenario, the Mediterranean route has been one of the most used by illegal boats. METHODOLOGY: In this report, the state of physical and psychological health of a cluster of Eritrean migrants, escaped from Libya and rescued in the Mediterranean Sea after a shipwreck, was described by epidemiological, clinical and laboratory investigations. RESULTS: Data suggest that despite the majority of the migrants being apparently in good health upon a syndromic surveillance approach, most of them suffered a decline in psychological status as well as severe malnutrition. The emergence of infectious diseases, related to poor living conditions during the journey, is not a rare event. CONCLUSION: The present report highlights the risks of failures of the syndromic medical approach in the setting of the extremely challenging migration route and underlines migrant frailties consequent to a prolonged journey and long period of detention. These stressors, which can degrade the initial health condition of traveling migrants, can lead to a premature "exhausted migrant effect" that should be carefully investigated in order to avoid the early emergence of diseases related to frailty.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".