Evidence map›Paper›PMID 41145678›Full record

ArticleClinical oral investigations2025

Predictive bite force modelling of head and neck oncology patients for clinical mandibular reconstruction applications.

Jorn-Ids Heins, Barzi Gareb, Reinier Ten Brink, Nathalie Vosselman, Gyorgy B Halmos, Anastasiia O Krushynska, Bram B J Merema, Joep Kraeima, Max J H Witjes

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PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical oral investigations, 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

9 authors.

Jorn-Ids HeinsDepartment of Oral and Maxillofacial Surgery, University of Groningen, University Medical Center Groningen, Groningen, 9713 GZ, The Netherlands. j.i.heins@umcg.nl.
Barzi GarebDepartment of Oral and Maxillofacial Surgery, University of Groningen, University Medical Center Groningen, Groningen, 9713 GZ, The Netherlands.
Reinier Ten BrinkDepartment of Oral and Maxillofacial Surgery, University of Groningen, University Medical Center Groningen, Groningen, 9713 GZ, The Netherlands.
Nathalie VosselmanDepartment of Oral and Maxillofacial Surgery, University of Groningen, University Medical Center Groningen, Groningen, 9713 GZ, The Netherlands.
Gyorgy B HalmosDepartment of Otorhinolaryngology, Head and Neck Surgery, University of Groningen, University Medical Center Groningen, Groningen, 9713 GZ, The Netherlands.
Anastasiia O KrushynskaEngineering and Technology Institute Groningen, University of Groningen, Groningen, 9747AG, The Netherlands.
Bram B J MeremaDepartment of Oral and Maxillofacial Surgery, University of Groningen, University Medical Center Groningen, Groningen, 9713 GZ, The Netherlands.
Joep KraeimaDepartment of Oral and Maxillofacial Surgery, University of Groningen, University Medical Center Groningen, Groningen, 9713 GZ, The Netherlands.
Max J H WitjesDepartment of Oral and Maxillofacial Surgery, University of Groningen, University Medical Center Groningen, Groningen, 9713 GZ, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveSince the introduction of individualized planning using 3D technology, the necessity for an individualized predictive bite force model has become increasingly critical for optimizing functional rehabilitation, and for tailoring surgical interventions to each head and neck oncology patient's unique anatomical and biomechanical profile. Current predictive models often lack comprehensive predictors and robust statistical methodologies, limiting their clinical applicability. To overcome these challenges, a predictive model of head and neck oncology patients' maximum voluntary bite force was developed by analyzing key anatomical and physiological factors.

resultsData were collected from 41 head and neck cancer patients, with 109 bite force observations analyzed using a linear mixed-effects model. The identified key predictors include body weight, superficial masseter muscle volume, bite force measurement region, and maxilla and/or mandibular teeth mobility. Superficial masseter muscle volume showed a positive association with bite force, while increased mobility of the teeth in the maxilla or mandible impacted bite force negatively.

conclusionThe model demonstrated strong explanatory performance (conditional R² = 0.777). CLINICAL RELEVANCE: This model enables patient-specific treatment planning and implant design for head and neck reconstructive surgery by implementing the bite forces in a finite element software used for computer aided designs. For instance, by tailoring mandibular reconstruction plates to individual bite forces using novel materials or designs, the model allows for a balance between sufficiently strong designs while preventing stress shielding seen with excessively strong designs. This study provides valuable insights into the multifactorial nature of bite force and its implications for patient care.

Indexed as

Bite ForceHead and Neck NeoplasmsMandibular ReconstructionAdultAgedBiomechanical PhenomenaFemaleFinite Element AnalysisHumansMaleMasseter MuscleMiddle AgedBite forceLinear modelsMagnetic resonance imagingMandibular reconstructionMasseter muscleMuscles

Identifiers

PMID41145678
PMCPMC12559112

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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.