Symptom Trajectories After COVID Hospitalization and Risk Factors for Symptom Burden in Older Persons: a Longitudinal Cohort Study
Bibliographic record
Abstract
BACKGROUND: Little is known about how psychosocial factors and geriatric conditions contribute to persistent post-COVID symptoms among older adults. We evaluated symptom burden following COVID-19 hospitalization and identified risk factors for persistent symptoms among -community-dwelling older adults. METHODS: This prospective study recruited 281 older persons (mean age 70.6 years) hospitalized for SARS-CoV-2 infection between June 2020 and June 2021 from Yale-New Haven Health System. Post-COVID symptoms were assessed using a modified Edmonton Symptom Assessment System during hospitalization, and at 1, 3, and 6 months post-discharge. Trajectory analysis identified three symptom trajectories. Multinomial logistic regression evaluated associations between characteristics (sociodemographic, clinical, psychosocial factors, and geriatric conditions) obtained during hospitalization and trajectory membership. RESULTS: Three symptom burden trajectory groups were identified: low (n = 70; 24.9%; reference); moderate (n = 149; 53.0%); and high (62; 22.1%). Female sex (adjusted odds ratio (adjOR)_moderate = 3.10 [95% CI = 1.68-5.72]; adjOR_high = 5.76 [2.70-12.27]), higher depression/anxiety (adjOR_moderate = 1.47 [1.24-1.74]; adjOR_high = 1.72 [1.43-2.07]), and less social support (adjOR_moderate = 0.91 [0.83, 0.99]; adjOR_high = 0.86 [0.78-0.95]) were associated with moderate and high symptom burden. Geriatric conditions, including delirium (adjOR_high = 7.74 [1.56-38.26]), frailty (adjOR_high = 5.26 [1.77-15.68]), impairment of physical function (adjOR_high = 1.18 [1.00-1.40]), and vision impairment (adjOR_high = 4.63 [1.33-16.11]), were associated with high symptom burden. CONCLUSIONS: In older persons hospitalized with COVID-19, female sex, psychosocial factors, and geriatric conditions were associated with higher symptom burden over six months. Future work should investigate the biopsychosocial mechanisms through which psychosocial factors and geriatric conditions contribute to post-COVID symptom burden.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".