Evidence map›Paper›PMID 31182188›Full record

RevieweLife2019

A comprehensive review of randomized clinical trials in three medical journals reveals 396 medical reversals.

Diana Herrera-Perez, Alyson Haslam, Tyler Crain, Jennifer Gill, Catherine Livingston, Victoria Kaestner, Michael Hayes, Dan Morgan, Adam S Cifu, Vinay Prasad

Abstract readReview
In one paragraph

Review in eLife, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

39 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Andexanet alfa: trials just leave us with more questions.Research and practice in thrombosis and haemostasis · 2025
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Recommendation Reversals in Gastroenterology Clinical Practice Guidelines.Journal of the Canadian Association of Gastroenterology · 2022
    Article
  16. Review
  17. Article
  18. Article
  19. Ethical Machine Learning in Healthcare.Annual review of biomedical data science · 2021
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Diana Herrera-Perez *Knight Cancer Institute, Oregon Health & Science University, Portland, United States.
Alyson Haslam *Knight Cancer Institute, Oregon Health & Science University, Portland, United States.ORCID 0000-0002-7876-3978
Tyler Crain *Knight Cancer Institute, Oregon Health & Science University, Portland, United States.
Jennifer Gill *Knight Cancer Institute, Oregon Health & Science University, Portland, United States.ORCID 0000-0002-5591-6855
Catherine LivingstonSchool of Medicine, Oregon Health & Science University, Portland, United States.
Victoria KaestnerKnight Cancer Institute, Oregon Health & Science University, Portland, United States.
Michael HayesDivision of Internal Medicine, Oregon Health & Science University, Portland, United States.
Dan MorganDepartment of Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, United States.
Adam S CifuDepartment of Medicine, University of Chicago, Chigcago, United States.
Vinay PrasadKnight Cancer Institute, Oregon Health & Science University, Portland, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ability to identify medical reversals and other low-value medical practices is an essential prerequisite for efforts to reduce spending on such practices. Through an analysis of more than 3000 randomized controlled trials (RCTs) published in three leading medical journals (the Journal of the American Medical Association, the Lancet, and the New England Journal of Medicine), we have identified 396 medical reversals. Most of the studies (92%) were conducted on populations in high-income counties, cardiovascular disease was the most common medical category (20%), and medication was the most common type of intervention (33%).

Indexed as

HumansOutcome Assessment, Health CarePeriodicals as TopicRandomized Controlled Trials as TopicResearch DesignResearch Reportevidence-based medicinehumanlow-value caremedical reversal

Identifiers

PMID31182188
PMCPMC6559784

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.