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Effect of Mouthguards on the Transmission of Force Across the Human Jaw

2005· article· en· W2024179949 on OpenAlexafffund
David Dong Hyun Lim, Stephen N. Robinovitch, David Goodman

Bibliographic record

VenueClinical Journal of Sport Medicine · 2005
Typearticle
Languageen
FieldHealth Professions
TopicDental Trauma and Treatments
Canadian institutionsSimon Fraser University
FundersSimon Fraser University
KeywordsMouthguardStiffnessChinMedicineOrthodonticsStructural engineeringDentistryAnatomyEngineering

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine if impact responses (stiffness and damping values) of the jaw and neck are significantly different between mouthguards of varying material and manufacturing properties, in controlled conditions involving specified upper limb orientation and applied loading. DESIGN: Quasi-experimental design. SETTING: Simon Fraser University biomechanical laboratory. PARTICIPANTS: Twelve male subjects between the ages of 19 to 28 participated in this study. All subjects were healthy, had no missing teeth, and had no mechanical (orthodontic) appliances. MAIN OUTCOME MEASUREMENTS: For each condition, the system stiffness and damping values were estimated using the free vibration technique. Stiffness and damping values are relevant because they are the major determinants of impact force and, potentially, injury risk. It is the first study to show experimentally the quantitative effects of mouthguard in situ. RESULTS: All the mouthguards lowered the system stiffness as compared with the no mouthguard condition (P < 0.001). There was no observed effect on stiffness between the 2 limb orientation positions. Excitation weight had an unexpected effect on system stiffness (P = 0.041), with increasing weight leading to increased stiffness. CONCLUSIONS: The findings suggest that wearing any mouthguard is better than wearing none at all due to their ability to reduce system stiffness and damping values after a blow to the chin.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.121
GPT teacher head0.566
Teacher spread0.445 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2005
Admission routes2
Has abstractyes

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