Development of the Canadian COVID-19 Emergency Department Rapid Response Network population-based registry: a methodology study
Bibliographic record
Abstract
BACKGROUND: Emergency physicians lack high-quality evidence for many diagnostic and treatment decisions made for patients with suspected or confirmed coronavirus disease 2019 (COVID-19). Our objective is to describe the methods used to collect and ensure the data quality of a multicentre registry of patients presenting to the emergency department with suspected or confirmed COVID-19. METHODS: This methodology study describes a population-based registry that has been enrolling consecutive patients presenting to the emergency department with suspected or confirmed COVID-19 since Mar. 1, 2020. Most data are collected from retrospective chart review. Phone follow-up with patients at 30 days captures the World Health Organization clinical improvement scale and contextual, social and cultural variables. Phone follow-up also captures patient-reported quality of life using the Veterans Rand 12-Item Health Survey at 30 days, 60 days, 6 months and 12 months. Fifty participating emergency departments from 8 provinces in Canada currently enrol patients into the registry. INTERPRETATION: Data from the registry of the Canadian COVID-19 Emergency Department Rapid Response Network will be used to derive and validate clinical decision rules to inform clinical decision-making, describe the natural history of the disease, evaluate COVID-19 diagnostic tests and establish the real-world effectiveness of treatments and vaccines, including in populations that are excluded or underrepresented in clinical trials. This registry has the potential to generate scientific evidence to inform our pandemic response, and to serve as a model for the rapid implementation of population-based data collection protocols for future public health emergencies. TRIAL REGISTRATION: Clinicaltrials.gov, no. NCT04702945.
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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 | 0.141 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".