Trajectories of health-related quality of life and their predictors in adult COVID-19 survivors: A longitudinal analysis of the Biobanque Québécoise de la COVID-19 (BQC-19)
Bibliographic record
Abstract
PURPOSE: A significant number of people will experience prolonged symptoms after COVID-19 infection that will greatly impact functional capacity and quality of life. The aim of this study was to identify trajectories of health-related quality of life (HRQOL) and their predictors among adults diagnosed with COVID-19. METHODS: This is a retrospective analysis of an ongoing prospective cohort study (BQC-19) including adults (≥18y) recruited from April 2020 to March 2022. Our primary outcome is HRQOL using the EQ-5D-5L scale. Sociodemographic, acute disease severity, vaccination status, fatigue, and functional status at onset of the disease were considered as potential predictors. The latent class mixed model was used to identify the trajectories over an 18-month period in the cohort as a whole, as well as in the inpatient and outpatient subgroups. Multivariable and univariable regressions models were undertaken to detect predictors of decline. RESULTS: 2163 participants were included. Thirteen percent of the outpatient subgroup (2 classes) and 28% in the inpatient subgroup (3 classes) experienced a more significant decline in HRQOL over time than the rest of the participants. Among all patients, age, sex, disease severity and fatigue, measured on the first assessment visit or on the first day after hospital admission (multivariable models), were identified as the most important predictors of HRQOL decline. Each unit increase in the SARC-F and CFS scores increase the likelihood of belonging to the declining trajectory (univariable models). CONCLUSION: Although to different degrees, similar factors explain the decline in HRQOL over time among the overall population, people who have been hospitalized or not. Clinical functional capacity scales could help to determine the risk of HRQOL decline.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".