Evaluation of Large Language Models for Automated Simple

Eve R Glenn, Ariana Rowshan, Eric Mao, David Ryu, Yesha Parekh, Nigel N Hsu, John M Thompson, Amiethab A Aiyer

Abstract read

In one paragraph

Article in Foot & ankle orthopaedics, 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

8 authors.

Eve R GlennDepartment of Orthopaedic Surgery, Johns Hopkins University, Baltimore, Maryland, USA.ORCID https://orcid.org/0009-0007-4056-8943

Ariana RowshanDepartment of Orthopaedic Surgery, Johns Hopkins University, Baltimore, Maryland, USA.ORCID https://orcid.org/0000-0002-0194-2396

Eric MaoDepartment of Orthopaedic Surgery, Johns Hopkins University, Baltimore, Maryland, USA.

David RyuDepartment of Orthopaedic Surgery, Johns Hopkins University, Baltimore, Maryland, USA.ORCID https://orcid.org/0000-0002-6943-5681

Yesha ParekhDepartment of Orthopaedic Surgery, Johns Hopkins University, Baltimore, Maryland, USA.

Nigel N HsuDepartment of Orthopaedic Surgery, Johns Hopkins University, Baltimore, Maryland, USA.ORCID https://orcid.org/0000-0002-3592-7004

John M ThompsonDepartment of Orthopaedic Surgery, Johns Hopkins University, Baltimore, Maryland, USA.

Amiethab A AiyerDepartment of Orthopaedic Surgery, Johns Hopkins University, Baltimore, Maryland, USA.ORCID https://orcid.org/0000-0001-5070-2770

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Accurate Methods: Twenty-one common procedures identified by a single Results: Perplexity achieved the highest accuracy (92.9%, 95% CI = 87.4%-98.4%), whereas Deepseek V3 performed worst (48.2%, 95% CI = 37.5%-58.9%). Global χ Conclusion: LLM performance in

Indexed as

artificial intelligenceautomated medical codingbilling accuracyCPT codingLarge language models

Identifiers

PMID42169810

DOI10.1177/24730114261448207

PMCPMC13187371

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC

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.