Using resources wisely in the COVID-19 pandemic: an international list of Choosing Wisely recommendations
Bibliographic record
Abstract
BACKGROUND: The novel coronavirus (COVID-19) pandemic has brought forth issues of health system resource limitations into urgent matters of public importance. Our objective was to rapidly develop an international Choosing Wisely list of recommendations for clinicians and the public about using resources wisely in the COVID-19 pandemic. METHODS: Choosing Wisely Canada convened a rapid Delphi process to develop an international Choosing Wisely list for COVID-19. Informed by a rapid review of emerging literature, this process engaged a small group of clinicians and public advisors in Canada (n=10) and internationally (n=8) to develop a candidate list of recommendations. A survey with candidate recommendations was sent to Choosing Wisely clinician leaders in Canada and around the world. Based on survey results and input, list recommendations were modified. RESULTS: The survey was sent to 293 potential respondents and received a 56% response rate in 72 hours (n=163). Respondents were asked to score each of the 9 recommendations on a 5-point Likert scale ranging from 1 strongly disagree to 5 strongly agree followed by free text comment. There were 271 total comments across 9 recommendations. Overall, all 9 recommendations had high levels of agreement with 83%-96% of respondents ranking them as strongly agree or agree. INTERPRETATION: This list of recommendations provides evidence-based statements about using resources wisely in the COVID-19 pandemic. The list reflects international consensus on evidence-based recommendations for both clinicians and the public which were achieved through a rapid consensus building process. TRIAL REGISTRATION: n/a
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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 | 0.124 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".