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Record W4391105805 · doi:10.4317/jced.61200

Orodental injury and mouthguard usage in Dutch and international field hockey

2024· article· en· W4391105805 on OpenAlexfundno aff
KE. van Vliet, Jan de Lange, H.S. Brand, Frank Lobbezoo

Bibliographic record

VenueJournal of Clinical and Experimental Dentistry · 2024
Typearticle
Languageen
FieldHealth Professions
TopicDental Trauma and Treatments
Canadian institutionsnot available
FundersInstitute of Musculoskeletal Health and ArthritisXenios
KeywordsMouthguardField hockeyMedicinePoison controlPhysical therapyInjury preventionHuman factors and ergonomicsMedical emergencyAdvertisingBusiness

Abstract

fetched live from OpenAlex

Background: Mouthguards are used to prevent players from orodental injuries in field hockey. However, such injuries are still a common problem. This study describes the prevalence of orodental injury and the related mouthguard usage in field hockey. Material and Methods: A 19-item questionnaire was distributed in the Dutch field hockey competition and at the international Master World Cup. In total, 1213 questionnaires were collected. Standard descriptive statistics were used to describe the samples. Associations between data were determined using the Pearson Chi-Square test. Results: The prevalence of orodental injuries during the career of hockey players was 20% in Dutch players, and 29% in international players. Mouthguard usage among Dutch players was 95%, and among international players 88%. There was no significant association between wearing a mouthguard or not with respect to whether or not treatment was requested as a result of an orodental injury (Dutch p=0.43; international p=0.22). Conclusions: This study showed that the prevalence of orodental injuries in field hockey are high, while the majority of the players use a 'protective' mouthguard. These results imply that the current mouthguards may not provide enough protection against the forces used in field hockey.

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)0.000
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 score0.023
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.129
GPT teacher head0.565
Teacher spread0.436 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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