Long-Term Functional Limitations and Predictors of Recovery After COVID-19: A Multicenter Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Limited data exist on post-severe COVID-19 functional trajectory, particularly considering premorbid status. We characterized 1-year functional recovery post-hospitalization for COVID-19, highlighting predictors of long-term recovery. METHODS: We enrolled adult patients with lab-confirmed SARS-CoV-2 infection and hospitalized for COVID-19 sequelae, from five major Ontario, Canada hospitals in a prospective cohort study. Assessments included telephone interviews on admission followed by telephone and in-person assessments at 3-, 6-, 9-, and 12-months post-discharge. The Activity-Measure for Post-Acute Care (AM-PAC) Mobility and Cognition scales were administered at baseline and every 3 months for 1 year. Secondary outcomes included symptoms, spirometry, physical performance, dyspnea, fatigue, distress, anxiety and depression, and quality of life. RESULTS: A total of 254 patients (57.1% male) with a mean age of 60.0 (±13.1) years and an average hospital stay of 14.3 (±19.7) days agreed to participate. At 12 months, 55.3% demonstrated clinically important deficits in mobility and 38.8% had cognitive deficits compared to premorbid levels. Fatigue was reported in 44.2%, followed by difficulty walking long distances in 35.8% and dyspnea in 33.0%. Almost 40% of patients had an FEV1(% Pred) < 80% at 12 months, 60.3% had impairments in physical performance, and 44.5% had problems with anxiety or depression. Predictors of better mobility at 12 months included higher premorbid mobility status, male sex, shorter hospital stay, fewer comorbidities, and higher FEV1 (% pred) at the 3-month follow-up. CONCLUSIONS: Our study provides compelling evidence of the long-term impact of COVID-19 on functional and cognitive status 1-year post-infection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".