Evidence map›Paper›PMID 37038147›Full record

ArticleTrials2023

Specific recommendations to improve the design and conduct of clinical trials.

Mark J Kupersmith, Nathalie Jette

Abstract readLetter
In one paragraph

Article in Trials, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Trial
  2. Placebo-resistant gut bacteria:Frontiers in cellular and infection microbiology · 2024
    Trial
  3. Review
  4. GutWomen's health reports (New Rochelle, N.Y.) · 2024
    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

2 authors.

Mark J KupersmithDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Mark.kupersmith@mountsinai.org.
Nathalie JetteDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Funding

Statistical Unsupervised Learning VF for IIHTT & ONTTR21EY032522 · NEI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KUPERSMITH, MARK J · 2021 to 2022
$495k
NEI NIH HHS EY032522NEI NIH HHS R21 EY032522
6 · The paper itself

Abstract

There are many reasons why the majority of clinical trials fail or have limited applicability to patient care. These include restrictive entry criteria, short duration studies, unrecognized adverse drug effects, and reporting of therapy assignment preferential to actual use. Frequently, experimental animal models are used sparingly and do not accurately simulate human disease. We suggest two approaches to improve the conduct, increase the success, and applicability of clinical trials. Studies can apply dosing of the investigational therapeutics and outcomes, determined from animal models that more closely simulate human disease. More extensive identification of known and potential risk factors and confounding issues, gleaned from recently organized "big data," should be utilized to create models for trials. The risk factors in each model are then accounted for and managed during each study.

Indexed as

Clinical Trials as TopicResearch DesignAnimalsHumans

Identifiers

PMID37038147
PMCPMC10084694

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.