Evidence map›Paper›PMID 42266004›Full record

ArticleAnatomy & cell biology2026

Application of machine learning for sex estimation using the first and second ribs in the Northern Thai population.

Napadol Poomkert, Tawachai Monum, Sukon Prasitwattanaseree, Pasuk Mahakkanukrauh

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Article in Anatomy & cell biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

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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.

Napadol PoomkertMaster of Science Program in Forensic Science, School of Multidisciplinary and Interdisciplinary School, Chiang Mai University, Chiang Mai, Thailand.ORCID https://orcid.org/0009-0008-9065-904X
Tawachai MonumDepartment of Forensic Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.ORCID https://orcid.org/0000-0001-6621-9150
Sukon PrasitwattanasereeDepartment of Statistics, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand.ORCID https://orcid.org/0000-0002-1987-773X
Pasuk MahakkanukrauhDepartment of Anatomy, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.ORCID https://orcid.org/0000-0003-0611-7552

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sex estimation is a fundamental component of forensic biological profiling, yet primary markers such as the pelvis and skull are frequently fragmented or absent due to taphonomic factors. This study investigates the diagnostic efficacy of the first and second atypical ribs for sex estimation within a Northern Thai population using advanced machine learning (ML). Osteometric variables, 15 for the first rib and 14 for the second were recorded from 200 right side ribs (100 males, 100 females) and 177 left side ribs (100 males, 77 females) at Chiang Mai University. Twelve ML algorithms, including ensemble stacking and voting classifiers, were refined via recursive feature elimination with cross-validation (RFECV) and hyperparameter optimization through the Optuna framework. Results demonstrated pronounced sexual dimorphism, with male dimensions consistently exceeding those of females across nearly all variables (

Indexed as

First and second ribForensic anthropologyMachine learningSex estimationThai population

Identifiers

PMID42266004
PMCPMC13616760

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.