Understanding the Experience of Long COVID Symptoms in Hospitalized and Non-Hospitalized Individuals: A Random, Cross-Sectional Survey Study
Bibliographic record
Abstract
The relationship between initial COVID-19 infection and the development of long COVID remains unclear. The purpose of this study was to compare the experience of long COVID in previously hospitalized and non-hospitalized adults in a community-based, cross-sectional telephone survey. Participants included persons with positive COVID-19 test results between 21 March 2021 and 21 October 2021 in Alberta, Canada. The survey included 330 respondents (29.1% response rate), which included 165 previously hospitalized and 165 non-hospitalized individuals. Significantly more previously hospitalized respondents self-reported long COVID symptoms (81 (49.1%)) compared to non-hospitalized respondents (42 (25.5%), p < 0.0001). Most respondents in both groups experienced these symptoms for more than 6 months (hospitalized: 66 (81.5%); non-hospitalized: 25 (59.5), p = 0.06). Hospitalized respondents with long COVID symptoms reported greater limitations on everyday activities from their symptoms compared to non-hospitalized respondents (p < 0.0001) and tended to experience a greater impact on returning to work (unable to return to work—hospitalized: 20 (19.1%); non-hospitalized: 6 (4.5%), p < 0.0001). No significant differences in self-reported long COVID symptoms were found between male and female respondents in both groups (p > 0.05). This study provides novel data to further support that individuals who were hospitalized for COVID-19 appear more likely to experience long COVID symptoms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".