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Record W3191573995 · doi:10.3390/children8080653

Neighborhood Contexts and Oral Health Outcomes in a Pediatric Population: An Exploratory Study

2021· article· en· W3191573995 on OpenAlexafffundabout
Vladyslav A. Podskalniy, Sharat Chandra Pani, Jinhyung Lee, Liliani Aires Candido Vieira, Hiran Perinpanayagam

Bibliographic record

VenueChildren · 2021
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsWestern University
FundersSchulich School of Medicine and Dentistry
KeywordsSocioeconomic statusMedicineCensusPopulationDental careOral healthDemographyUnit (ring theory)Environmental healthFamily medicinePsychology

Abstract

fetched live from OpenAlex

AIMS: This study aimed to explore the impacts of neighborhood-level socioeconomic contexts on the therapeutic and preventative dental quality outcome of children under 16 years. MATERIALS AND METHODS: Anonymized billing data of 842 patients reporting to a university children's dental over three years (March 2017-2020) met the inclusion criteria. Their access to care (OEV-CH-A), topical fluoride application (TFL-CH-A) and dental treatment burden (TRT-CH-A) were determined by dental quality alliance (DQA) criteria. The three oral health variables were aggregated at the neighborhood level and analyzed with Canadian census data. Their partial postal code (FSA) was chosen as a neighborhood spatial unit and maps were created to visualize neighborhood-level differences. RESULTS: = 0.001) and the cost of dental care. While there was no significant association between neighborhood-level sociodemographic variables and OEV-CH-A, TRT-CH-A showed a significant negative association at the neighborhood level with median household income and significant positive association with percentage of non-official first language (English or French) speakers. CONCLUSION: Initial analysis suggests differences exist in dental outcomes according to neighborhood-level sociodemographic variables, even when access to dental care is similar.

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 score0.021
Threshold uncertainty score0.997

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 integrity0.0000.000
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 head0.024
GPT teacher head0.337
Teacher spread0.313 · 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
Published2021
Admission routes3
Has abstractyes

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