Factors associated with death and ICU referral among COVID-19 patients hospitalized in the secondary referral academic hospital in East Jakarta, Indonesia
Bibliographic record
Abstract
Introduction: We present demographic and clinical characteristics including laboratory and imaging data of COVID-19. Factors associated with death and ICU referral were evaluated. Methods: This is a retrospective cohort study of hospitalized COVID-19 patients confirmed by real time polymerase chain reaction (RT-PCR). Logistic regression was used to evaluate the associations between demographic and clinical characteristics with the outcomes. Results: A total of 477 patients have been hospitalized from October 2020 - February 2021, 112 patients were over 60 years old and 58.2% were women. There were 299 (62.7%) patients with clinical improvement and negative RT-PCR at discharge, 145 (30.4%) patients with clinical improvement and positive RT-PCR at discharge, 14 (2.9%) patients referred to ICU, and 19 (4%) patients died. The most common clinical symptoms were fever, cough, nausea and vomiting, and shortness of breath. Multivariate logistic regression analysis revealed age ≥60 years old, shortness of breath, obesity, oxygen saturation <95%, leukocyte count >10,000/L, and moderate-severe COVID-19 at admission were strongly associated with death or referral to ICU. Conclusion: Patients aged over 60 years old with obesity, low peripheral oxygen saturation, high leucocyte count, shortness of breath and moderate-severe COVID-19 at admission had higher risks of death or referred to ICU.
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How this classification was reachedexpand
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 | 0.008 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.007 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".