Long-term Consequences of COVID-19 and the Pandemic: Protocol for a Web-Based, Longitudinal Observational Study (DEFEAT)
Bibliographic record
Abstract
BACKGROUND: With population-wide vaccination availability, the global COVID-19 pandemic entered a new phase. Despite vaccination status, some people who were infected with SARS-CoV-2 experience long-term symptoms. OBJECTIVE: In this study, we aim to characterize the long-term effects of SARS-CoV-2 infection and the pandemic. We also aim to build symptom clusters and determine risk factors for developing long COVID symptoms. Furthermore, we assess social participation and health-related quality of life in patients with long COVID and in the general population during a global pandemic. METHODS: With a mixed-methods, web-based approach, we aim to recruit 2000 people in Germany who are older than 18 years and can provide informed consent. In the quantitative arm of the study, we identify symptoms of and predictive factors for long COVID manifestations with cluster analysis and assess social participation during the pandemic with standardized questionnaires. The qualitative arm of the study uses individual interviews and focus group discussions to better understand the illness experience of persons who experience long COVID. RESULTS: Recruitment started in September 2021. Up until July 2022, we recruited approximately 4500 participants via our web-based database. CONCLUSIONS: This study aims to build an innovative, patient-centered, web-based research platform appropriate for the pandemic by minimizing physical contact between study personnel and participants. All study activities are designed to better understand the long COVID syndrome, social participation during the pandemic, and the illness experiences of persons affected by long COVID. TRIAL REGISTRATION: German Clinical Trial Registry DRKS00026007; https://tinyurl.com/yh282fkt. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/38718.
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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 | 0.035 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.046 | 0.013 |
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".