Investigating patient-reported outcomes among COVID-19 survivors : a longitudinal cohort study
Bibliographic record
Abstract
Background: Many Coronavirus Disease 2019 (COVID-19) survivors report long-term sequelae. However, few studies have measured patient-reported outcomes and compared them to those of patients who tested negative for severe acute respiratory syndrome coronavirus-2 (SARS-COV-2). This study compares the long-term physical and mental health outcomes of patients presenting to emergency departments who tested positive for SARS-COV-2 with those who tested negative. Methods: This study enrolled consecutive eligible patients presenting to emergency departments participating in the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN) between March 1st 2020 and July 14th 2021. Patients tested for SARS-COV-2 were eligible. Consecutive SARS-COV-2 positive patients were consented for follow-up, and matched with test-negative controls from the same hospital and date. The outcome measures were the Veterans RAND physical health component score (PCS) and mental health component score (MCS). The PCS and MCS of propensity score matched patients were analyzed using linear mixed effects models. Risk factors for PCS and MCS were modelled using linear regression. Results: Our cohort included 1170 SARS-COV-2 positive patients and 3716 test-negative controls. Comparing the groups, the adjusted mean difference in PCS was 0.50 (95%CI: -0.36, 1.36) and -1.01 (95%CI: -1.91, -0.11) for MCS. A World Health Organization Ordinal Outcome Score of 6-7, representing severe SARS-COV-2 disease, was the strongest predictor of PCS (β=-7.4; 95%CI: -9.8, -5.1). Prior mental health illness was the strongest predictor of MCS (β=-5.4; 95%CI: -6.3, -4.5). Conclusion: The mean PCS was similar among SARS-COV-2 positive and negative participants tested under similar circumstances, while mean MCS was worse among SARS-COV-2 positive participants. The mental health sequelae of COVID-19 should be considered when developing long-term support programs for survivors.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".