Cross-sectional study of SARS-CoV2 clinical characteristics in an immigrant population attended in a Hospital Emergency Department in the Catalunya Health Region in Spain
Why this work is in the frame
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Bibliographic record
Abstract
AIM: The COVID pandemic has been the biggest health challenge faced in decades. The aim of this study is to assess the characteristics of immigrant patients who attended a Hospital Emergency Department during the first three waves of the coronavirus pandemic. METHODS: A retrospective, descriptive study of immigrant patients treated in a Hospital Emergency Department between March 15 and November 30, 2020. A descriptive analysis and a comparative analysis were carried out according to place of origin, gender and age. For the comparative analysis, the chi-square test for qualitative variables was used. For the comparative analysis according to gender, Student's t test or the Mann-Whitney U test was used for normal or non-normal quantitative variables, respectively. The Kruskal-Wallis test was used for normal or non-normal quantitative variables according to age. RESULTS: We have analyzed 633 immigrant patients who visited the emergency department during the study period. Of the sample, 50.1% patients were women and 78% of all patients came from Africa. The mean age of the patients was 44.1 years. Most patients (72.5%) were discharged to home after evaluation in the emergency department, especially European patients. One-quarter of patients required social resources to be able to comply with quarantine measures, of whom 87% were African. Forty-seven percent of patients became infected at home and 41% in the workplace. CONCLUSIONS: The immigrant population is generally younger and less infected than the population at large. In addition, the use of social resources to guarantee patient isolation has often proved essential in controlling outbreaks that have arisen in these communities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it