Low-Value Care: Convergence and Challenges Comment on "Key Factors That Promote Low-Value Care: Views From Experts From the United States, Canada, and the Netherlands"
Bibliographic record
Abstract
Interest has increased in the topic of de-implementation, ie, reducing so-called low-value care (LVC). The article "Key Factors That Promote Low-Value Care: Views From Experts From the United States, Canada, and the Netherlands" by Verkerk and colleagues identifies national-level factors affecting LVC use in those three countries. This commentary raises three critical points regarding the study. First, the study does not clearly define the national level. Secondly, national-level factors might not be relevant for all types of LVCs and thirdly, the study's rather limited sample makes it difficult to draw firm conclusions. We also include some critical comments related to some of the study's findings in relation to results of our recently published scoping review of the international literature on de-implementation and use of LVC and an interview study with primary care physicians on LVC use. Finally, we provide some suggestions for further research that we believe is needed to improve understanding of LVC use and facilitate its de-implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.048 | 0.052 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".