The COVID-19 Early Detection in Doctors and Healthcare Workers (CEDiD) Study: study protocol for a prospective observational trial
Bibliographic record
Abstract
Abstract Background The global COVID-19 pandemic has caused worldwide disruption with its exponential spread mandating national and international lockdown measures. Hospital-associated transmission has been identified as a major factor in the perpetuation of COVID-19, with healthcare workers at high-risk of becoming infected with SARS-CoV-2 and representing important vectors for spread, but not routinely having their clinical observations monitored or being tested for COVID-19. Methods A single-center, prospective observational study of 60 healthcare workers will explore how many healthcare workers in high-risk areas develop COVID-19 infection over a thirty day period. High-risk areas are defined as COVID positive wards, the intensive care unit or the accident and emergency department. Healthcare workers (HCWs) will be recruited and have daily self-administered nasopharyngeal SARS-CoV-2 PCR tests. They will also be provided with a wearable medical device to measure their clinical observations during non-working hours, and be asked to complete a daily self-reported symptom questionnaire over the study period. Statistical analysis will assess the proportion of healthcare workers who develop COVID-19 infection as a primary objective, with secondary objectives exploring what symptoms are developed, time-to-event, and deviations in clinical observations. Discussion At present clinical observations, symptoms and COVID-19 PCR swabs are not routinely undertaken for healthcare workers. If the CEDiD (COVID-19 Early Detection in Doctors and Healthcare Workers) study is successful, it will provide useful information for workforce decisions in reducing hospital-associated transmission of COVID-19. The data will help in determining whether there are early warning signs for development of COVID-19 infections amongst healthcare workers and may contribute to the evidence base advocating for more regular testing of healthcare workers’ observations, symptoms and COVID-19 status. Trial registration ClinicalTrials.gov, NCT04363489 . Registered on 27 th July 2020
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".