Evidence map›Paper›PMID 34344325›Full record

ArticleBMC cancer2021

The frequency of medical reversals in a cross-sectional analysis of high-impact oncology journals, 2009-2018.

Alyson Haslam, Jennifer Gill, Tyler Crain, Diana Herrera-Perez, Emerson Y Chen, Talal Hilal, Myung S Kim, Vinay Prasad

Abstract read
In one paragraph

Article in BMC cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. 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

8 authors.

Alyson HaslamDepartment of Epidemiology and Biostatistics, University of California, San Francisco, USA.
Jennifer GillProvidence Health & Services, Portland, OR, USA.
Tyler CrainDepartment of Analytics, Northwest Permanente, Portland, OR, USA.
Diana Herrera-PerezOregon Health & Science University, Portland, OR, USA.
Emerson Y ChenOregon Health & Science University, Portland, OR, USA.
Talal HilalUniversity of Mississippi Medical Center, Jackson, MS, USA.
Myung S KimOregon Health & Science University, Portland, OR, USA.
Vinay PrasadDepartment of Epidemiology and Biostatistics, University of California, San Francisco, USA. vinayak.prasad@ucsf.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIdentifying ineffective practices that have been used in oncology is important in reducing wasted resources and harm. We sought to examine the prevalence of practices that are being used but have been shown in RCTs to be ineffective (medical reversals) in published oncology studies.

methodsWe cross-sectionally analyzed studies published in three high-impact oncology medical journals (2009-2018). We abstracted data relating to the frequency and characterization of medical reversals.

resultsOf the 64 oncology reversals, medications (44%) represented the most common intervention type (39% were targeted). Fourteen (22%) were funded by pharmaceutical/industry only and 56% were funded by an organization other than pharmaceutical/industry. The median number of years that the practice had been in use prior to the reversal study was 9 years (range 1-50 years).

conclusionHere we show that oncology reversals most often involve the administration of medications, have been practiced for years, and are often identified through studies funded by non-industry organizations.

Indexed as

Medical OncologyCross-Sectional StudiesHumansPeriodicals as TopicPublicationsResearchLow-value careMedical reversalOncology

Identifiers

PMID34344325
PMCPMC8336285

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.