Evidence map›Paper›PMID 41268359›Full record

ArticleHealth science reports2025

Identification of Population Affinity Using Dental Traits: A Narrative Review in Forensic Odontology.

Alok Atreya, Ritesh G Menezes, Srinivasa Rao Bolla, Samarika Dahal

Abstract read
In one paragraph

Article in Health science reports, 2025. 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

4 authors.

Alok AtreyaDepartment of Forensic Medicine Lumbini Medical College Palpa Nepal.ORCID https://orcid.org/0000-0001-6657-7871
Ritesh G MenezesForensic Medicine Division, Department of Pathology, College of Medicine Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia.ORCID https://orcid.org/0000-0002-2135-4161
Srinivasa Rao BollaDepartment of Biomedical Sciences, School of Medicine Nazarbayev University Astana Kazakhstan.ORCID https://orcid.org/0000-0002-2644-5169
Samarika DahalDepartment of Oral Pathology and Forensic Dentistry Tribhuvan University Institute of Medicine Maharajgunj Medical Campus Kathmandu Nepal.ORCID https://orcid.org/0000-0001-8512-0850

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Aim: Forensic odontology utilizes dental morphology to estimate population affinity in medico-legal cases. This review examines the role of dental traits in human identification, emphasizing their limitations in conclusively estimating ancestry or ethnicity. Methods: A comprehensive search was conducted across databases such as PubMed, ScienceDirect, Scopus and Web of Science using key terms - race, ancestry, ethnicity, population affinity and forensic odontology. Literature studying race/ancestry (older studies) and population affinity (newer studies) was reviewed. Results: All dental features are characterized by a certain degree of variation across human populations. On one hand, tooth characteristics may seem useful for identifying ancestry and ethnicity. Yet, combining dental traits with other evidence may improve ancestry estimation. Training observers with additional sources and practical skills improves accuracy and produces more reliable results. The observational method, while systematic, risks oversimplifying contextual factors. However, in forensic odontology, the broader social context of this approach must be considered, as it evaluates whether race is a biological notion. Conclusions: Traditional terms like race and ancestry should be replaced by population affinity. Future recommendations include developing improved methods to enhance result accuracy.

Indexed as

ancestryCarabelli's cuspforensic odontologyodontometrypopulation affinity

Identifiers

PMID41268359
PMCPMC12626902

What Socratic holds

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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 Socratic graph.