Evidence map›Paper›PMID 41257216›Full record

ArticleProceedings of machine learning research2025

Caught in the Web of Words: Do LLMs Fall for Spin in Medical Literature?

Hye Sun Yun, Karen Y C Zhang, Ramez Kouzy, Iain J Marshall, Junyi Jessy Li, Byron C Wallace

Abstract read
PubMed Full text at PMC
In one paragraph

Article in Proceedings of machine learning research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. When AI agrees with us: large language models as amplifiers of scientific bias.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026
    Article
  2. 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

6 authors.

Hye Sun YunNortheastern University, Boston, MA, USA.
Karen Y C ZhangNortheastern University, Boston, MA, USA.
Ramez KouzyThe University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Iain J MarshallKing's College London, London, UK.
Junyi Jessy LiThe University of Texas at Austin, Austin, Texas, USA.
Byron C WallaceNortheastern University, Boston, MA, USA.

Funding

Building Safety Guards into LLMs for Trustworthy Automatic Simplification of Medical DocumentsR01LM014600 · NLM · UNIVERSITY OF TEXAS AT AUSTIN · PI Junyi Jessy Li, BYRON CASEY WALLACE · 2024 to 2026
$1.0M
NLM NIH HHS R01 LM014600
6 · The paper itself

Abstract

Medical research faces well-documented challenges in translating novel treatments into clinical practice. Publishing incentives encourage researchers to present "positive" findings, even when empirical results are equivocal. Consequently, it is well-documented that authors often

Identifiers

PMID41257216
PMCPMC12622377

What OpenQuestion holds

Textmetadata
LicenceTDM
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.