Evidence map›Paper›PMID 38351205›Full record

ArticleInternational journal of legal medicine2024

Human identification: an investigation of 3D models of paranasal sinuses to establish a biological profile on a modern UK population.

Madeline Robles, Sherry Nakhaeizadeh, Carolyn Rando, Ruth M Morgan

Open access · hybridAbstract read
In one paragraph

Article in International journal of legal medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
17.5field-weighted citation impact, top 1% 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

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Observational
  3. Article
  4. 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 at 1 institution in 1 country.

Madeline RoblesUCL Department of Security and Crime Science, 35 Tavistock Square, London, WC1H 9EZ, UK. madeline.robles@uwe.ac.uk.
Sherry NakhaeizadehUCL Department of Security and Crime Science, 35 Tavistock Square, London, WC1H 9EZ, UK.
Carolyn RandoUCL Institute of Archaeology, 31-34 Gordon Square, London, WC1H 0PY, UK.
Ruth M MorganUCL Department of Security and Crime Science, 35 Tavistock Square, London, WC1H 9EZ, UK.
University College London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medical imaging is a valuable source for facilitating empirical research and provides an accessible gateway for developing novel forensic anthropological methods for analysis including 3D modelling. This is especially critical for the United Kingdom (UK), where methods developed from modern UK populations do not currently exist. This study introduces a new approach to assist in human identification using 3D models of the paranasal sinuses. The models were produced from a database of 500 modern CT scans provided by University College London Hospital. Linear measurements and elliptic Fourier coefficients taken from 1500 three-dimensional models across six ethnic groups assessed by one-way ANOVA and discriminant function analysis showed a range of classification rates with certain rates reaching 75-85.7% (p < 0.05) in correctly classifying age and sex according to size and shape. The findings offer insights into the potential for employing paranasal sinuses as an attribute for establishing the identification of unknown human remains in future crime reconstructions.

Indexed as

Forensic AnthropologyImaging, Three-DimensionalParanasal SinusesTomography, X-Ray ComputedAdolescentAdultAgedAged, 80 and overDiscriminant AnalysisEthnicityFemaleHumansMaleMiddle AgedUnited KingdomYoung Adult3D modellingComputed tomographyForensic anthropologyForensic scienceParanasal sinuses

Identifiers

PMID38351205
PMCPMC11164725
OpenAlexW4391772550

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.