An external validation of the QCOVID3 risk prediction algorithm for risk of hospitalisation and death from COVID-19: An observational, prospective cohort study of 1.66m vaccinated adults in Wales, UK
Bibliographic record
Abstract
INTRODUCTION: At the start of the COVID-19 pandemic there was an urgent need to identify individuals at highest risk of severe outcomes, such as hospitalisation and death following infection. The QCOVID risk prediction algorithms emerged as key tools in facilitating this which were further developed during the second wave of the COVID-19 pandemic to identify groups of people at highest risk of severe COVID-19 related outcomes following one or two doses of vaccine. OBJECTIVES: To externally validate the QCOVID3 algorithm based on primary and secondary care records for Wales, UK. METHODS: We conducted an observational, prospective cohort based on electronic health care records for 1.66m vaccinated adults living in Wales on 8th December 2020, with follow-up until 15th June 2021. Follow-up started from day 14 post vaccination to allow the full effect of the vaccine. RESULTS: The scores produced by the QCOVID3 risk algorithm showed high levels of discrimination for both COVID-19 related deaths and hospital admissions and good calibration (Harrell C statistic: ≥ 0.828). CONCLUSION: This validation of the updated QCOVID3 risk algorithms in the adult vaccinated Welsh population has shown that the algorithms are valid for use in the Welsh population, and applicable on a population independent of the original study, which has not been previously reported. This study provides further evidence that the QCOVID algorithms can help inform public health risk management on the ongoing surveillance and intervention to manage COVID-19 related risks.
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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".