Evidence map›Paper›PMID 42500058›Full record

ArticleWorld journal of otorhinolaryngology - head and neck surgery2026

Clarity Without Credibility? Human Versus AI Abstracts in Otolaryngology.

Sholem Hack, Rebecca Attal, Lirit Levi, David T Liu, Mahdokht Manavi, Luca Giovanni Locatello, Maxime Fieux, Evan M de Joya, Joel M Gutovitz, Shahar Eliahu and 3 more

Abstract read
In one paragraph

Article in World journal of otorhinolaryngology - head and neck surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

13 authors.

Sholem HackCity St. George's University London School of Medicine, Program Delivered by University of Nicosia at the Chaim Sheba Medical Center Ramat Gan Israel.ORCID https://orcid.org/0009-0001-0651-6994
Rebecca AttalCity St. George's University London School of Medicine, Program Delivered by University of Nicosia at the Chaim Sheba Medical Center Ramat Gan Israel.
Lirit LeviDepartment of Otolaryngology - Head and Neck Surgery Stanford University School of Medicine Stanford California USA.
David T LiuDepartment of Otolaryngology - Head and Neck Surgery Medical University of Vienna Vienna Austria.
Mahdokht ManaviDepartment of Otolaryngology - Head and Neck Surgery Stanford University School of Medicine Stanford California USA.
Luca Giovanni LocatelloDepartment of Otorhinolaryngology Academic Hospital "Santa Maria della Misericordia", Azienda Sanitaria Universitaria Friuli Centrale Udine Italy.
Maxime FieuxHospices Civils de Lyon, Hôpital Lyon Sud, Service d'ORL, d'otoneurochirurgie et de Chirurgie Cervico-faciale Pierre Bénite France.
Evan M de JoyaDepartment of Otorhinolaryngology-Head and Neck Surgery McGovern Medical School at the University of Texas Health Science Center Houston Texas USA.
Joel M GutovitzDepartment of Ophthalmology University of Missouri Kansas City Kansas City Missouri USA.
Shahar EliahuFaculty of Medicine Tel Aviv University Tel Aviv Israel.
Tal HefetzFaculty of Medicine Tel Aviv University Tel Aviv Israel.
Tomer BoldesFaculty of Medicine Tel Aviv University Tel Aviv Israel.
Ameen BiadseeFaculty of Medicine Tel Aviv University Tel Aviv Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study evaluated whether otolaryngologists can distinguish between human- and machine-written abstracts. The primary question was whether large language models (LLMs) produce abstracts comparable in clarity and usefulness to human-authored work, and whether reviewers can identify authorship with accuracy. Methods: A blinded cross-sectional design was used. Forty-eight abstracts were evaluated, consisting of twenty-four human-authored abstracts and 24 generated by four LLMs. Human abstracts were drawn from articles published after July 2025 to minimize overlap with LLM training data. Twenty otolaryngologists independently reviewed all abstracts. Using a structured rubric, raters classified authorship, rated clarity, usefulness, and confidence on 5-point scales, and provided optional free-text explanations. Group comparisons were performed using chi-square and Mann-Whitney tests, with Kruskal-Wallis tests for model-level analyses. Results: Overall recognition accuracy was 44.7%. Human-written abstracts were more often misclassified as AI than AI-generated abstracts were mistaken for human. Human abstracts received significantly higher clarity and usefulness scores than LLM abstracts, though effect sizes were small. Confidence did not correlate with correctness, indicating miscalibration of rater judgments. Model-level performance varied. Grok-generated abstracts were most easily identified as AI, whereas GPT-5 and Claude 3.5 more frequently resembled human writing. Free-text rationales commonly referenced style, vagueness, or lack of detail when AI authorship was suspected. Conclusion: LLMs generate abstracts that increasingly resemble human scientific writing, yet still lag in perceived usefulness and credibility. Clinicians were only moderately successful at detecting authorship and were frequently confident in incorrect classifications. These findings highlight both the promise and risks of AI-assisted scientific communication.

Indexed as

abstract qualityauthorship detectionlarge language modelsotolaryngologypeer reviewscientific publishing

Identifiers

PMID42500058
PMCPMC13398884

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.