The boundaries between survival and nonsurvival at COVID‐19: Experience of tertiary care pandemic hospital
Bibliographic record
Abstract
OBJECTIVE: Coronavirus disease 2019 (COVID-19) is an emerging, fast-spreading, highly mortal and worldwide infectious disease. The pulmonary system was defined as the main target of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but the mortality concept of this disease presented with more severe and systemic disease. The present study investigated the relationship between the patient characteristics at the initial hospital administration and fatality in COVID-19 patients. METHODS: In this retrospective and comparative cohort study, all the 767 hospitalised COVID-19 patients, treated between 18 March and 15 May 2020 in the Covid Clinics of Gulhane Training and Research Hospital in Ankara, Turkey, were evaluated. RESULTS: The fatality rate was significantly increased in patients with any comorbid disease except asthma. The initial laboratory test results indicated highly significant differences according to the patient's outcome. A multifactor logistic regression analysis was performed to calculate the adjusted odds ratios for predicting patient outcomes. Being older than 60 years increased the death risk with an adjusted OR of 7.2 (95% CI: 2.23-23.51; P = .001). The presence of a cancer and the extended duration of intensive care unit treatment were other significant risk factors for nonsurvival. Azithromycin treatment was determined as significantly reduced the death ratio in these patients (P = .002). CONCLUSION: It was revealed that being older than 60 years, presence of a cancer and extended duration of ICU treatment were the major risk factors for predicting fatality rate in hospitalised COVID-19 patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".