Evidence map›Paper›PMID 42208088›Full record

ArticleBrazilian journal of otorhinolaryngology

Oropharyngeal cancer management in the era of artificial intelligence for optimal decision-making.

Mario Augusto Ferrari de Castro, Rogério Aparecido Dedivitis, Leandro Luongo de Matos, Caio Paschoalin Trindade, Bruno Pelinson Fogaça Duarte, Luiz Paulo Kowalski

Abstract read
In one paragraph

Article in Brazilian journal of otorhinolaryngology. 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

6 authors.

Mario Augusto Ferrari de CastroUniversidade Metropolitana de Santos, Santos, SP, Brazil.
Rogério Aparecido DedivitisHospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), Department of Head and Neck Surgery, São Paulo, SP, Brazil. Electronic address: dedivitis.hns@uol.com.br.
Leandro Luongo de MatosHospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), Instituto do Câncer do Estado de São Paulo (ICESP), Department of Head and Neck Surgery, São Paulo, SP, Brazil.
Caio Paschoalin TrindadeHospital Ana Costa, Department of Head and Neck Surgery, Santos, SP, Brazil.
Bruno Pelinson Fogaça DuarteUniversidade Metropolitana de Santos, Santos, SP, Brazil.
Luiz Paulo KowalskiHospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), Department of Head and Neck Surgery, São Paulo, SP, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo assess the accuracy of AI systems in informing clinical decision-making processes for the management of oropharyngeal malignancies.

methodsStructured inquiries derived from established clinical recommendations for oropharyngeal cancer management were formulated. These inquiries were systematically processed through multiple Large Language Model (LLM) platforms. The Claude platform was specifically employed to identify discrepancies between established guideline recommendations and LLM-generated responses. Subsequently, a panel of three subject matter experts conducted an assessment of these identified differences to evaluate their potential clinical significance.

resultsAnalysis of the 28 generated responses revealed that 21 cases (75%) showed congruence between LLM outputs and established guideline recommendations. In three instances, the guidelines provided significantly more comprehensive content than LLM responses. One response from the LLM contained additional generated relevant information, while three responses were contradictory to the guidelines.

conclusionThe LLM demonstrated 75% concordance with guideline recommendations and should be considered a complementary tool for established clinical guidelines. LEVEL OF EVIDENCE: Level 3.

Indexed as

Artificial IntelligenceClinical Decision-MakingOropharyngeal NeoplasmsHumansLarge Language ModelsPractice Guidelines as TopicAI artificial intelligenceCarcinomaClinical practice guidelineOropharyngeal cancerSquamous cell

Identifiers

PMID42208088
PMCPMC13240728

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.