The proof is in the pandemic: real-world evidence on COVID-19
Bibliographic record
Abstract
Real-world data (RWD) refers to health information collected from different sources, such as electronic health records and wearable devices. Real-world evidence (RWE) is the insight gained by analysing RWD. This thesis explores applications of RWE in the context of the COVID-19 pandemic. Part 1 investigates the occurrence of myocarditis and pericarditis following the mRNA-1273 COVID-19 vaccine, using data from four European countries. The findings suggested that male sex, younger age, previous SARS-CoV-2 infection, and prior poor health may increase the risk of post-mRNA-1273 myocarditis and pericarditis. It was also found that patients who were older and had worse prior health were more likely to experience severe outcomes following myocarditis or pericarditis onset. Part 2 presents results of the COVID-RED trial, which investigated whether a wearable device could detect SARS-CoV-2 infections and enrolled nearly 18,000 participants. An algorithm based on data from the wearable device was able to detect many infections, even before symptom onset, but also produced to numerous false positive alerts. An analysis of study retention during the trial suggested that age, employment situation, living situation, and receipt of COVID-19 vaccination were linked to retention. Part 3 reviews 30 studies that investigated the use of federated learning, which is a method that allows for analysing RWD from different sources without sharing sensitive information, in infectious disease research. While promising for infectious disease research, the method still needs further development. Overall, this thesis demonstrates how RWE can improve our understanding of COVID-19 and prepare us for future outbreaks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.071 | 0.330 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".