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Record W2882436616 · doi:10.4103/ijpvm.ijpvm_376_17

Preliminary investigation of a novel mouthguard

2018· article· en· W2882436616 on OpenAlexaff
Les Kalman

Bibliographic record

VenueInternational Journal of Preventive Medicine · 2018
Typearticle
Languageen
FieldHealth Professions
TopicDental Trauma and Treatments
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineMouthguardData scienceDentistryComputer science

Abstract

fetched live from OpenAlex

Background: Mouthguards (MGs) remain an important piece of personal protection for athletes for the prevention of injury. Although MGs provide tremendous benefits, the design and fabrication process does not record the position of the mandible or the occlusion, which could lead to injury. This study compared a novel MG to over-the-counter (OTC) and custom MGs on a skull model. Methods: The OTC MG was formed as per manufacturer's guidelines, the custom MG was laboratory fabricated, and the novel MG was fabricated through a proprietary process. Each group of the three MGs was assessed for vertical dimension change, occlusal contacts, and condylar displacement. Results: Average number of occlusal contacts for the OTC, custom and novel MG were 2.4, 4.0, and 10, respectively. There was a significant difference between all values (P < 0.05). Average change in vertical dimension for the OTC, custom, and novel MG were 15.3 mm, 9.3 mm, and 8.0 mm, respectively. The novel MG value was significantly different (P < 0.05). The average distance of condylar displacement for the OTC, custom and novel MG were 1.9 mm, 1.3 mm and 0.6 mm, respectively. Conclusions: The novel MG was significantly different (P < 0.05). The data from this preliminary investigation suggests that the novel mouthguard had maximized occlusal contacts, minimized vertical dimension change and condylar displacement as compared to OTC and custom MGs.

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)0.001
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 score0.157
Threshold uncertainty score0.871

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.113
GPT teacher head0.476
Teacher spread0.363 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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