Impact of Underlying Chronic Medical Conditions on COVID-19 Outcomes Among People Living with HIV: A Retrospective Analysis from the Minnesota Fairview Network
Bibliographic record
Abstract
(1) Background: The Coronavirus Disease 2019 (COVID-19) pandemic has raised concerns about the impact of underlying medical conditions on the health outcomes of people living with HIV (PLWH). This study aimed to assess how pre-existing chronic medical conditions affect the health outcomes of PLWH infected with COVID-19. (2) Methods: A retrospective study using data from the Minnesota Fairview network (1 January 2020–31 December 2022) was conducted. Fisher’s exact test, the Kruskal–Wallis rank-sum test, and ordinal logistic regressions with a Benjamini–Hochberg (BH) adjustment on p-values were used to assess the influence of chronic conditions on COVID-19 severity, adjusting for age and gender. (3) Results: Among 216 records, significant associations were found for a stroke, chronic kidney disease, lung disease, and neurologic conditions (p < 0.05). Type 1 diabetes was marginally significant (0.05 < p < 0.1). After adjusting for age and sex, a stroke (p = 0.0008, BH-adjusted p = 0.0044) and chronic kidney disease (p = 0.0003, BH-adjusted p = 0.0033) significantly increased the risk of severe COVID-19 outcomes. (4) Conclusions: Pre-existing medical conditions should be considered in the clinical management and public health interventions for PLWH infected with COVID-19. Tailored strategies are essential to mitigate the higher risk of severe outcomes in PLWH with specific chronic comorbidities.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".