Evidence map›Paper›PMID 37345106›Full record

ArticleCancers2023

Development of a Machine Learning Model to Predict Recurrence of Oral Tongue Squamous Cell Carcinoma.

Yasaman Fatapour, Arash Abiri, Edward C Kuan, James P Brody

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
6.1field-weighted citation impact, top 3% of its field
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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 18 citations in OpenAlex.

  1. Machine learning based prediction of recurrence in oral tongue cancer: a systematic review with quantitative synthesis.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026
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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 at 1 institution in 1 country.

Yasaman FatapourDepartment of Biomedical Engineering, University of California, Irvine, CA 92617, USA.ORCID 0000-0001-8660-1621
Arash AbiriDepartment of Biomedical Engineering, University of California, Irvine, CA 92617, USA.ORCID 0000-0003-2656-1060
Edward C KuanDepartment of Otolaryngology-Head and Neck Surgery, University of California, Irvine, CA 92604, USA.
James P BrodyDepartment of Biomedical Engineering, University of California, Irvine, CA 92617, USA.ORCID 0000-0002-7995-5197
University of California, Irvine · US

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM008620 · NIGMS · UNIVERSITY OF CALIFORNIA-IRVINE · PI GOLDIN, ALAN L · 1999 to 2023
$8.1M
NIH HHS T32GM008620
6 · The paper itself

Abstract

Despite diagnostic advancements, the development of reliable prognostic systems for assessing the risk of cancer recurrence still remains a challenge. In this study, we developed a novel framework to generate highly representative machine-learning prediction models for oral tongue squamous cell carcinoma (OTSCC) cancer recurrence. We identified cases of 5- and 10-year OTSCC recurrence from the SEER database. Four classification models were trained using the H

Indexed as

artificial intelligencecancer recurrencemachine learningoral canceroral tongue squamous cell carcinoma

Identifiers

PMID37345106
PMCPMC10216090
OpenAlexW4376954218

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.