Evidence map›Paper›PMID 42262603›Full record

ArticleJournal of medical systems2026

Large Language Models vs. Machine Learning on Structured Perioperative Data: Does Model Choice Matter?

Theodora Wingert, Xuezhi Dong

Abstract readLetter
PubMed Publisher
In one paragraph

Article in Journal of medical systems, 2026. 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

2 authors.

Theodora WingertDepartment of Anesthesiology and Perioperative Medicine, University of California Los Angeles, 757 Westwood Plaza, Suite 3325, Los Angeles, CA, USA. twingert@mednet.ucla.edu.ORCID http://orcid.org/0000-0001-7149-9150
Xuezhi DongDepartment of Anesthesiology and Perioperative Medicine, University of California Los Angeles, 757 Westwood Plaza, Suite 3325, Los Angeles, CA, USA.

Funding

NIBIB NIH HHS 1K08EB038393
6 · The paper itself

Abstract

The study by Ko and colleagues provides evidence that large language models may achieve modestly improved performance compared with traditional machine learning models when applied to structured perioperative data. However, whether such incremental model performance gains can be translated into improvements in clinical decision-making, workflow integration, or patient outcomes remains an important question for future investigation.

Indexed as

Large Language ModelsMachine LearningHumansASA physical statusLarge language modelsMachine learningPrediction

Identifiers

PMID42262603

What OpenQuestion holds

Textmetadata
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.