Mitigating the Severity of COVID-19 Illness in the Primary Care Patient Population through Early Identification and Close Monitoring of Underlying Comorbidities
Bibliographic record
Abstract
Abstract Purpose Prior studies have identified risk factors which prognosticate severity of SARS-CoV-2 illness among hospitalized patients. Since the majority of patients first present to ambulatory care sites, there is a need to identify early predictors of disease progression in this population. Methods This retrospective cohort study investigated the impact of underlying comorbid conditions on SARS-CoV-2 infection severity in the ambulatory setting. All patients who presented to a single federally qualified health center (FQHC) between March-May 2020 with a positive SARS-CoV-2 test were reviewed for inclusion. Patient demographics, symptomology, prior medical history, and outcomes were collected. Results 301 patients were included, with nearly equal numbers of patients with (n=151) and without (n=150) underlying comorbidities. Overall, 269 patients (89%) had a mild outcome and 32 patients (11%) had a severe outcome. Advanced age (OR: 9.4 [95% CI: 3.4-27.4], p < 0.001) and male gender (OR: 3.2 [95% CI: 1.2-9.8], p = 0.02) were significant predictors of severe outcomes. Additionally, every obesity category (1: BMI = 30.0–34.9; 2: BMI = 35–39.9; 3: BMI = 40.0+) was associated with more severe outcomes compared to non-obese (OR: 3.5, p = 0.05; OR: 5.2, p = 0.03; OR: 13.9, p = 0.01). Compared to an HbA1C < 6, an HbA1C of 7.1–8.0 showed a clinically significant association. Conclusion SARS-CoV-2 severity can be prognosticated in the ambulatory population by the presence and severity of pre-existing comorbidities. Early identification and risk stratification of these comorbidities will allow clinicians to develop plans for closer monitoring to prevent severe illness.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".