Evidence map›Paper›PMID 40642210›Full record

ArticleFrontiers in neurology2025

Unsupervised machine learning revealed a correlation between low-dose statins and favorable outcomes in ICH patients.

Chaohua Cui, Haoye Guan, Tonghua Long, Yuru Liang

Abstract read
In one paragraph

Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

4 authors.

Chaohua Cui *Life Science and Clinical Medicine Research Center, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, China.
Haoye Guan *Life Science and Clinical Medicine Research Center, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, China.
Tonghua LongLife Science and Clinical Medicine Research Center, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, China.
Yuru LiangLife Science and Clinical Medicine Research Center, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Statins were effective in preventing ischemic stroke in patients with intracerebral hemorrhage (ICH). Statins administered at normal doses may contribute to hematoma expansion. We employed unsupervised machine learning methods and found that low-dose statins improve the prognosis of ICH. Methods: We employed unsupervised machine learning techniques to analyze unidentified factors within a retrospective cohort related to the prognosis of ICH. In our prospective cohort study, data were gathered from ICH patients over a three-year period. Outcomes included mortality, cerebrovascular events within 3 years, unfavorable functional outcomes at 3 months, and hematoma expansion during hospitalization. Results: The heatmap illustrated distinct statin effect trajectories, emphasizing the substantial impact of low-dose statins on prognosis. The low-dose statin group exhibited reduced mortality ( Conclusion: Unsupervised machine learning revealed a correlation between low-dose statins and patient prognosis. Subsequent analyses and validation indicated that for patients with intracerebral hemorrhage (ICH) in the cerebral hemisphere, early administration of low-dose hydrophilic statins is associated with reduced mortality, and statins are associated with a reduced rate of ischemic stroke occurrence. For all ICH patients, low-dose statins are associated with a lower rate of unfavorable functional outcomes but show no significant association with hematoma expansion.

Indexed as

cerebrovascular eventsintracerebral hemorrhagelow dose statinmortalityunsupervised machine learning

Identifiers

PMID40642210
PMCPMC12241808

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.