Characterizing Risk Factors for Hospitalization and Clinical Characteristics in a Cohort of COVID-19 Patients Enrolled in the GENCOV Study
Bibliographic record
Abstract
The GENCOV study aims to identify patient factors which affect COVID-19 severity and outcomes. Here, we aimed to evaluate patient characteristics, acute symptoms and their persistence, and associations with hospitalization. Participants were recruited at hospital sites across the Greater Toronto Area in Ontario, Canada. Patient-reported demographics, medical history, and COVID-19 symptoms and complications were collected through an intake survey. Regression analyses were performed to identify associations with outcomes including hospitalization and COVID-19 symptoms. In total, 966 responses were obtained from 1106 eligible participants (87% response rate) between November 2020 and May 2022. Increasing continuous age (aOR: 1.05 [95%CI: 1.01-1.08]) and BMI (aOR: 1.17 [95%CI: 1.10-1.24]), non-White/European ethnicity (aOR: 2.72 [95%CI: 1.22-6.05]), hypertension (aOR: 2.78 [95%CI: 1.22-6.34]), and infection by viral variants (aOR: 5.43 [95%CI: 1.45-20.34]) were identified as risk factors for hospitalization. Several symptoms including shortness of breath and fever were found to be more common among inpatients and tended to persist for longer durations following acute illness. Sex, age, ethnicity, BMI, vaccination status, viral strain, and underlying health conditions were associated with developing and having persistent symptoms. By improving our understanding of risk factors for severe COVID-19, our findings may guide COVID-19 patient management strategies by enabling more efficient clinical decision making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".