Risk of readmission to the emergency department in mild COVID-19 outpatients with telehealth follow-up
Bibliographic record
Abstract
Introduction: To describe patients´ characteristics of confirmed COVID-19 with mild symptoms discharged home from the Emergency Department (ED) and followed using telemedicine, to estimate ED-readmission rates and hospitalization, and to explore associated factors with these clinical outcomes. Methods: We performed a retrospective cohort study in Hospital Italiano de Buenos Aires from June to August 2020, which included patients with mild COVID-19 symptoms, diagnosed with a positive result. Follow-up occurred from discharged until ED-readmission or 14 days. We estimate cumulative incidence using the Kaplan-Meier model and associated factors using logistic regression. Results: We included 1,239 patients, with a median of 41 years and 53.82% male. A total of 167 patients were readmitted to the ED within 14 days, with a global incidence rate of 13.08% (95%CI 11.32-15.08). Of these, 83 required hospitalization (median time from diagnosis 4.98 days), 5.98% was not related to any COVID-19 complication, and five patients died. After adjustment by confounders (age ≥65, sex, diabetes, hypertension, former smoking, active smoking, fever, diarrhea, and oxygen saturation), we found significant associations: former smoking (adjusted OR 2.09, 95% CI 1.31-3.34, p0 .002), fever (aOR 1.56, 95% CI 1.07-2.28, p0.002) and oxygen saturation (aOR 0.82, 95% CI 0.71-0.95, p0.009). Conclusion: The 13% rate of ED-readmission during 14 days of follow-up of mild symptomatic COVID-19 patients initially managed as outpatients with telehealth is highly significant in hospital management, quality performance, and patient safety.
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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 | 0.003 |
| 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) | 0.003 | 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".