Factors Associated with SARS-CoV-2 Positivity in Patients Treated at the Lambayeque Regional Hospital, Peru during a Pandemic Period
Bibliographic record
Abstract
The aim of this study was to provide additional data on mortality from COVID-19 with particular attention to the factors associated with the positivity of patients admitted to the Lambayeque Hospital in Peru. A retrospective cohort analysis was carried out to determine the clinical-epidemiological factors associated with positivity for SARS-CoV-2 in patients treated at the Lambayeque Regional Hospital during the health emergency period in the context of the COVID-19 pandemic. It was observed that, as the demographic age group increased, the percentage of seropositivity increased, with 66.8% of elderly adults testing positive, compared to 37.4% of children (p < 0.001). More seropositive men than women were evident (61.1% vs. 54.1%; p < 0.001). The most frequent symptom of patients with suspected COVID-19 was cough (65.0%). However, the symptoms with the greatest frequency of seropositive patients were ageusia (78.6%) and fever (77.6%); cough was one of the symptoms with the lowest (63.9%) (p-value < 0.001). The comorbidities with the most seropositive patients were obesity (80.7%) and diabetes mellitus (73.6%) (p-value < 0.001), different from the top comorbidity of heart disease (12.7%) in suspected COVID-19 patients. In terms of disease signs, abnormal findings on MRI (98.11%) and dyspnea (28.7%) were the most common in suspected COVID-19 patients, similar to those in seropositive patients, which were dyspnea (81.4%) and abnormal tomography findings (75.3%) (p-value < 0.001).
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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 | 0.001 |
| 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".