Evidence map›Paper›PMID 39601215›Full record

ArticleJournal of comparative effectiveness research2025

R WE ready for reimbursement? A round up of developments in real-world evidence relating to health technology assessment: part 17.

Paul Arora, Sreeram V Ramagopalan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of comparative effectiveness research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. 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.

Paul AroraDivision of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, M5T 3M7, Canada.
Sreeram V RamagopalanCentre for Pharmaceutical Medicine Research, King's College London, London, SE1 9NH, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this update, we discuss a position statement from the National Institute of Health and Care Excellence (NICE) on the use of artificial intelligence for evidence generation and publications reviewing the use of real-world data as external control arms. Finally, we discuss a number of recent studies investigating the real-world effectiveness of glucagon-like peptide-1 receptor agonists and whether these studies are informative for reimbursement decision making.

Indexed as

Artificial IntelligenceTechnology Assessment, BiomedicalComparative Effectiveness ResearchDecision MakingHumansReimbursement Mechanismsartificial intelligenceevidence generationexternal control armsGLP-1RAhealth technology assessmentnatural language processingNICEobesityreal-world datareal-world evidence

Identifiers

PMID39601215
PMCPMC11650383

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.