Evidence map›Paper›PMID 40457693›Full record

ArticleOral diseases2025

The Current State of Clinical Diagnostic Algorithms for Mucosal Oral Lesions: A Scoping Review.

Mohammed Al-Shehri, Theerthika Dillibabu, Belinda Nicolau, Marco Magalhaes, Nicholas Makhoul, Faleh Tamimi, Peter Chauvin, Sreenath Madathil

Abstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in Oral diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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.

Mohammed Al-ShehriFaculty of Dental Medicine and Oral Health Sciences, McGill University, Montréal, Quebec, Canada.
Theerthika DillibabuFaculty of Dental Medicine and Oral Health Sciences, McGill University, Montréal, Quebec, Canada.
Belinda NicolauFaculty of Dental Medicine and Oral Health Sciences, McGill University, Montréal, Quebec, Canada.
Marco MagalhaesOral Pathology and Oral Medicine, University of Toronto, Toronto, Ontario, Canada.ORCID https://orcid.org/0000-0002-5060-7738
Nicholas MakhoulMontreal General Hospital, Montreal, Quebec, Canada.
Faleh TamimiCollege of Dental Medicine, QU Health, Qatar University, Doha, Qatar.ORCID https://orcid.org/0000-0002-4618-8374
Peter ChauvinFaculty of Dental Medicine and Oral Health Sciences, McGill University, Montréal, Quebec, Canada.
Sreenath MadathilFaculty of Dental Medicine and Oral Health Sciences, McGill University, Montréal, Quebec, Canada.ORCID https://orcid.org/0000-0001-9406-6518

Funding

CIHR
6 · The paper itself

Abstract

backgroundDiagnosing oral lesions remains challenging for many dentists. Despite the availability of diagnostic algorithms, there is a dearth of comprehensive evidence synthesis and a discussion on their clinical and pedagogical applicability.

methodsA scoping review was conducted to identify: (1) algorithms or flow diagrams that help clinicians to diagnose oral lesions in a clinical setting without additional software; (2) publications in English; (3) all age groups; (4) algorithms for oral lesions of soft tissue only. We excluded those that are: (1) black-box; (2) required additional tests; (3) older versions; (4) for non-mucosal lesions, and (5) intended for self-screening. A keyword and MeSH term search was performed across three peer-reviewed publication databases and gray literature.

resultsSeventeen algorithms from 15 peer-reviewed manuscripts and 1 online course were identified. Most studies did not mention how the algorithms were developed, and none had been validated in a clinical setting. The algorithms often focused on one or two types of lesions and were incomplete in differential diagnoses.

conclusionFew clinical diagnostic algorithms for oral lesions are available in the literature. Notably, there are no validated and comprehensive clinical diagnostic algorithms for oral mucosal lesions.

Indexed as

AlgorithmsMouth DiseasesMouth MucosaDiagnosis, DifferentialHumansMouth Neoplasmsalgorithmsdiagnosisoral lesions

Identifiers

PMID40457693
PMCPMC12803634

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Registered trials

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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.