Development of a minimum data set for long COVID: a Delphi study protocol
Bibliographic record
Abstract
INTRODUCTION: Previous consensus-based long COVID research has focused on establishing research priorities, developing clinical definitions, core outcomes and a list of recommendations of patient-reported outcome measures that can be used to assess and characterise long COVID. Complementing and extending this work, the proposed study will bring together diverse knowledge users to prioritise concepts of care, quality of life and symptoms to inform a national patient registry on long COVID. METHODS AND ANALYSIS: We will conduct a Delphi process involving Canadians with lived experiences and/or professional expertise with long COVID (including clinicians, policymakers, caregivers and community leaders). A pool of long COVID survey questions has been established through an environmental scan; these questions were coded by topic and will be presented via a series of online, anonymous survey questionnaires to a diverse cohort of 100 participants. Over the course of three Delphi rounds, participants will prioritise and recommend topics related to care, quality of life and symptoms. We will use the prioritised topics to develop a list of core questions as a minimum data set to standardise data collection and inform a national patient registry on long COVID in Canada. ETHICS AND DISSEMINATION: This study has been approved by the University of Saskatchewan Behavioural Research Ethics Board (BEH #4296). Findings will be shared at national conferences and will be published in an open-access peer-reviewed journal. In addition, the minimum data set will be shared with key knowledge users as recommendations to inform a national long COVID patient registry.
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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 | 0.233 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.048 | 0.011 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".