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Record W2426526011 · doi:10.2174/1874210601610010315

Prevalence of Traumatic Dental Injuries in Patients Attending University of Alberta Emergency Clinic

2016· article· en· W2426526011 on OpenAlexaffabout
Thamer Alkhadra, William Preshing, Tarek El‐Bialy

Bibliographic record

VenueThe Open Dentistry Journal · 2016
Typearticle
Languageen
FieldHealth Professions
TopicDental Trauma and Treatments
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineDental traumaCrown (dentistry)DentistryTooth AvulsionLogistic regressionIncisorInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: This study evaluated the prevalence of dental trauma for patients attending the emergency dental clinic at the University of Alberta Hospital between 2006-2009. Patients' examination and treatment charts were reviewed. METHODS: Total number of patients' charts was 1893.The prevalence of different types of trauma was 6.4 % of the total cases (117 patients). Trauma cases were identified according to Ellis classification and as modified by Holland et al., 1988. RESULTS: Logistic statistical model showed that 21.7% were Ellis class I trauma, 16.7% were Ellis class II trauma, and 6.7% were Ellis class III. In addition, 11.7 % presented with avulsion, 7.5 % presented with dentoalveolar fracture and 7.5% presented with sublaxation. Also, 17.55 % presented with tooth displacement within the alveolar bone, 3.3 % presented with crown fracture with no pulp involvement, 4.16 % presented with crown fracture with pulp involvement and 3.3 % presented with root fracture. In conclusion, the general prevalence of dentoalveolar trauma in patients attending the emergency clinic at the University of Alberta is less than other reported percentages in Canada or other countries.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.441
Threshold uncertainty score0.877

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.077
GPT teacher head0.411
Teacher spread0.334 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2016
Admission routes2
Has abstractyes

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