Prediction of Stature From Percutaneous Anthropometric Dimensions of the Femur and Tibia Among Adult Nigerians
Notice bibliographique
Résumé
INTRODUCTION: Estimating stature, sex, ancestry, and age is central to forensic anthropological profiling, underpinning human identification in medico-legal contexts and mass-casualty events. Skeletal analysis informs these components. Although widely established in developed settings, Nigeria and many developing countries lack robust forensic anthropometric datasets despite rising disaster rates. This study sought to derive population‑specific regression equations to estimate stature from percutaneous femoral and tibial measurements in Nigerians. Regression analysis has proven to be the most straightforward and dependable approach for estimating stature. METHODS: This cross‑sectional observational study was conducted among 255 healthy Nigerian adults (130 males, 125 females; 18-65 years) recruited by stratified random sampling from the University of Lagos and Lagos University Teaching Hospital, Lagos, Nigeria, following ethical approval (approval number: CMUL/HREC/0955/19). Stature, femoral length, tibial length, and femoral bi‑epicondylar width were measured using standardized International Society for the Advancement of Kinanthropometry (ISAK) protocols with calibrated instruments (SECA™ stadiometer (Hamburg, Germany), Rosscraft calipers (Campbell, Canada), and Mitutoyo vernier calipers (Kawasaki, Japan). All measurements were taken by a single investigator at fixed times to minimize bias; intra‑observer reliability was assessed by triplicate readings, with mean values recorded. Bilateral measurements were averaged, and outliers were excluded if attributable to error or implausibility. Data were analyzed in IBM SPSS Statistics software, version 25 (IBM Corp., Armonk, NY) after normality and regression assumptions were verified, and sex‑specific and pooled regression models were developed to predict stature. RESULTS: The mean height of males was higher than females, reflecting clear sexual dimorphism in stature. Regression analysis demonstrated strong, statistically significant correlations between stature and femoral/tibial dimensions in both sexes. The pooled models yielded high coefficients of determination (R²) with low standard errors of estimate, indicating good predictive accuracy, with tibial length emerging as the most reliable predictor of stature, offering the greatest accuracy across both sexes (males standard error of estimate (SEE) ± 5.08 cm; females SEE ± 16.02 cm), whereas femoral intercondylar width contributed little to predictive value. CONCLUSION: This study establishes a significant correlation between lower limb dimensions and stature, aligning with trends reported in other groups. The dataset generated offers a valuable forensic reference to aid human identification, particularly in contexts involving fragmented or mutilated remains. By providing population‑specific standards, this work enhances the application of lower‑limb metrics in forensic practice within Nigeria. There is a need to broaden research across diverse Nigerian ethnic groups, conduct cross-validation on skeletal remains, and integrate advanced analytical tools to enhance applicability. Also, incorporating artificial intelligence and computational modelling offers further potential to refine accuracy and strengthen cross‑validation of regression models.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».