Developing a minimum dataset for a national patient registry on Long COVID in Canada: a Delphi consensus-based study
Bibliographic record
Abstract
OBJECTIVES: To develop survey items for a national patient registry on Long COVID using a modified Delphi process. DESIGN: This study was based on a modified Delphi process involving three rounds of anonymous, online surveys to develop consensus on and prioritise survey elements to be included in a minimum dataset for use in a national patient registry in Canada. Initial Long COVID items were identified through an environmental scan of the literature. SETTING: This study focused on healthcare systems in Canada and was conducted online. PARTICIPANTS: A panel of 52 experts (patients, caregivers, clinicians and researchers) participated in all three rounds of the online survey. These participants were recruited through the Long COVID Web network and word of mouth. RESULTS: In total, 243 survey elements related to care, quality of life and symptoms were included in round 1 of the survey. 200 reached consensus and moved to round 2 with two additional elements being developed based on open-ended responses. In round 2, participants ranked these survey elements and 34 advanced. In round 3, 33 survey elements met the threshold of consensus with one added a priori. The 33 survey elements were then used to develop a Long COVID minimum dataset, which consists of 48 items. CONCLUSIONS: The findings affirm broad consensus for collecting data related to fatigue, post-exertional malaise, cardiovascular issues, respiratory problems and cognitive issues. This highlighted the desire for quality-of-life indicators and information related to care utilisation, quality and access.
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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 | 0.252 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".