Tools to Reduce Low-Value Care: Lessons From COVID-19 Pandemic Comment on "Key Factors that Promote Low-Value Care: Views of Experts From the United States, Canada, and the Netherlands"
Bibliographic record
Abstract
Based on a summary of interviews with 18 experts, Verkerk et al defined the seven key factors that promoted low-value care, which included system, social, and knowledge factors. During the ongoing coronavirus disease 2019 (COVID-19) pandemic, these key factors have been influential due to the uncertainty of the disease at the beginning of the pandemic. Globally, several measures have been implemented to reduce low-value care practices and promote high-value care for COVID-19 patients. From huge multicenter, non-industry sponsored or multiplatform trials, to the use of social networks sites is an indispensable and effective way to disseminate medical information. Thanks to these measures, we have transformed a scenario of ignorance into an evidence-based medical scenario in less than a year. Verkerk and colleagues' proposed key factors are an excellent framework for characterizing and highlighting the lessons that can be learnt from how we have fought against the pandemic and low-value practices.
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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 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.059 | 0.057 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".