Choosing Wisely in pediatric healthcare: A narrative review
Bibliographic record
Abstract
Background: It has been estimated that 20% of the tests and therapies currently prescribed in North America are likely unnecessary, add no value, and may even cause harm. The Choosing Wisely (CW) campaign was launched in 2012 in the US and Canada to limit the overuse of medical procedures in adult and pediatric healthcare, to avoid overdiagnosis and overtreatment. Methods: In this narrative review, we describe the birth and spread of the CW campaign all over the world, with emphasis on CW in pediatric healthcare. Results: To date, CW has spread to more than 25 countries and 80 organizations, with 700 recommendations published. The awareness of medication overuse also made its way into pediatrics. One year after the launch of the CW campaign, the American Academy of Pediatrics and the pediatric section of the Society of Hospital Medicine provided the first recommendations specifically aimed at pediatricians. Thereafter, many European pediatric societies also became active in the CW campaign and published specific top-5 recommendations, although there is not yet a common set of CW recommendations in Europe. Discussion: We reviewed the main pediatric CW recommendations in medical and surgical fields and discussed how the recommendations have been produced, published, and disseminated. We also analyzed whether and how the CW recommendations impacted pediatric medical practice. Furthermore, we highlighted the common obstacles in applying CW recommendations, such as pressure from patients and families, diagnostic uncertainty, and worries about legal problems. Finally, we highlighted the necessity to foster the CW culture, develop an implementation plan, and measure the results in terms of overuse decline.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".