MétaCan
Menu
Back to cohort

Differences in the etiology of mandibular fractures in Kuwait, Canada, and Finland

2004· article· en· W2017120210 on OpenAlexaffabout
Kyösti Oikarinen, Petr Schütz, Lukman Thalib, George K.B. Sándor, Cameron M. L. Clokie, Tina Meisami, Slavek Safar, Mikko Moilanen, Mohammed Belal

Bibliographic record

VenueDental Traumatology · 2004
Typearticle
Languageen
FieldMedicine
TopicFacial Trauma and Fracture Management
Canadian institutionsToronto General Hospital
FundersKuwait University
KeywordsEtiologyFalling (accident)MedicinePoison controlOccupational safety and healthInjury preventionDentistryDemographyMedical emergencyEnvironmental healthPsychiatry

Abstract

fetched live from OpenAlex

We studied causes of mandibular fractures treated in oral and maxillofacial units in three countries in years 1990-2000 in Kuwait (n=596), 1995-2000 in Canada (n=228), and 1990-99 in Finland (n=268). Of the Finnish patients, 27% were women. Corresponding percentages in Kuwait and Canada were 13 and 17%, respectively. Traffic crashes were the cause of injury in 55% of the cases in Kuwait and 33% in Oulu, but only 7% in Toronto. In Kuwait, the victims were often young people, which is why more traffic education, more control of speed, and more control of the use of safety belts should be implemented. Assault was the cause in 54% in Toronto, 12% in Kuwait, and 37% in Oulu. Falling was the cause in 22% of the cases in Kuwait. Alcohol was implicated in 21% of cases in Canada and 15% in Finland.

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)0.003
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 score0.078
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.005
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.253
Teacher spread0.240 · 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

Citations39
Published2004
Admission routes2
Has abstractyes

Explore more

Same venueDental TraumatologySame topicFacial Trauma and Fracture ManagementFrench-language works237,207