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
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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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 | 0.000 | 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) | 0.001 | 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".