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Record W3015814971 · doi:10.1111/ipd.12651

A 10‐year retrospective study of paediatric emergency department visits for dental conditions in Montreal, Canada

2020· review· en· W3015814971 on OpenAlexaffabout
Beatriz Ferraz dos Santos, Basma Dabbagh

Bibliographic record

VenueInternational Journal of Paediatric Dentistry · 2020
Typereview
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsMcGill UniversityMontreal Children's Hospital
Fundersnot available
KeywordsMedicineEmergency departmentRetrospective cohort studyEmergency medicineMedical emergencyFamily medicinePediatricsNursingSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Trends of paediatric emergency visits (ED) for dental conditions have been broadly discussed; however, little has been published in the Canadian context. AIM: To describe the utilization of ED for dental conditions among children and to investigate demographic characteristics influencing its use. DESIGN: A comprehensive review of records of all children aged 1-17 years who attended the ED service of a paediatric hospital in Montreal, Canada, for dental conditions over a 10-year period (2004-2013) was completed. Information on the child's principal dental diagnosis, sociodemographic data, and source of referral was obtained. RESULTS: A total of 10 905 paediatric ED visits were seen during the study period. Among the children, 54.7% were male and the majority was younger than 6 years old. Dental caries constituted the most common reason for ED presentation comprising close to 43% of total visits for a dental complaint. Females, teenagers, and self-referred children were more likely to experience ED visits due to non-traumatic dental conditions. CONCLUSIONS: The utilization of ED for dental conditions has increased among pre-school children in the last decade and was mostly due to caries-related dental problems. Effective preventive strategies are needed to improve the oral health condition of children.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.446
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.020
GPT teacher head0.349
Teacher spread0.329 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations15
Published2020
Admission routes2
Has abstractyes

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