Evidence map›Paper›PMID 37559829›Full record

ArticleDigital health

Medical journals and advertiser tracking-Consequences for patients, clinicians, and editors.

Ravi Gupta, Ari B Friedman, Matthew S McCoy

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Ravi GuptaDivision of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.ORCID https://orcid.org/0000-0001-5902-6414
Ari B FriedmanLeonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA.
Matthew S McCoyLeonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medical journal websites frequently contain tracking code that transfers data about journal readers to third parties. These data give drug, device, and other medical product companies a potentially powerful resource for targeting advertisements and other marketing materials to journal readers based on unique attributes and medical interests that can be inferred from the articles they read. Thus, while editors may strictly regulate the content of advertisements that such companies place in their journals' pages, they simultaneously provide those companies with the means to target readers in other forums, possibly in ways that subvert editorial guidelines. We examine the implications of third-party tracking on medical journal webpages, and recommend actions that publishers, editors, and academic societies can take to curb it.

Indexed as

digital privacymedical journalsThird-party tracking

Identifiers

PMID37559829
PMCPMC10408310

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.