Factors Associated with Mortality in Patients with COVID-19 from a Hospital in Northern Peru
Bibliographic record
Abstract
We aimed to identify the factors associated with mortality in patients with COVID-19 from the hospitalization service of the Cayetano Heredia Hospital, Piura, Peru, from May to June 2020. A prospective study was conducted in hospitalized patients with a confirmed diagnosis of COVID-19 through serological and/or molecular reactive testing. The dependent variable was death due to COVID-19, and the independent variables were the epidemiological, clinical and laboratory characteristics of the patient. The chi-square test and the non-parametric Mann–Whitney U test were used, with a significance level of 5%. Of 301 patients with COVID-19, the majority of them were male (66.1%), and the mean age was 58.63 years. Of the patients analyzed, 41.3% of them died, 40.2% of them were obese and 59.8% of them had hepatic steatosis. The three most frequent signs/symptoms were dyspnea (90.03%), fatigue (90.03%) and a cough (84.72%). Being an older adult (p = 0.011), being hospitalized in the ICU (p = 0.001), overweight (p = 0.016), obese (p = 0.021) and having compromised consciousness (p = 0.039) and thrombocytopenia (p = 0.024) were associated with mortality due to COVID-19. Overall, the mortality rate due to COVID-19 was 41.3%. Having an older age, being hospitalized in the ICU, overweight, obese and having compromised consciousness and thrombocytopenia were positively associated with mortality in patients with COVID-19. These findings highlight the need to establish an adequate system of surveillance and epidemiological education in hospitals and communities in the event of new outbreaks, especially in rural and northern Peru.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".