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Incidence, Risk Factors, and Mortality Associated With Orofacial Cleft Among Children in Ontario, Canada

2020· article· en· W3006134807 on OpenAlexafffundabout
Claudia Malic, Melody Lam, Jessy Donelle, Lucie Richard, Simone N. Vigod, Eric I. Benchimol

Bibliographic record

VenueJAMA Network Open · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCleft Lip and Palate Research
Canadian institutionsWomen's College HospitalChildren's Hospital of Eastern OntarioInstitute for Clinical Evaluative SciencesUniversity of TorontoOttawa HospitalUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsIncidence (geometry)MedicineDemographyPediatricsEnvironmental health

Abstract

fetched live from OpenAlex

Importance: Orofacial cleft (OFC) is one of the most common congenital malformations, with a wide variation in incidence worldwide. However, population-based studies on the incidence of OFC in North America are lacking. Objectives: To examine the incidence of OFC in Ontario, Canada, and to compare risk factors and mortality associated with children with OFC vs children without OFC. Design, Setting, and Participants: This population-based retrospective cohort study used health administrative data from the province of Ontario, Canada. Children with OFC who were born from April 1, 1994, to March 31, 2017, in Ontario were each matched to 5 children without OFC based on sex, date of birth (±30 days), and mother's age (±5 years). Analyses were conducted from September 2018 to January 2019. Exposures: Children born with OFC. Main Outcomes and Measures: Incidence of OFC over time and regional variation. Risk factors for OFC were assessed using 1-way analysis of variance for means, Kruskal-Wallis for medians, and χ2 tests for categorical variables. Adjusted Cox regression models were used to assess mortality. Results: From 1994 to 2017, 3262 children were born with OFC in Ontario, Canada, and they were matched to 15 222 children born without OFC. Incidence of OFC in Ontario was 1.12 cases per 1000 live births, with wide geographic variation and a lower incidence from 2004 to 2017 compared with 1994 to 2003 (1.02 vs 1.13 cases per 1000 live births; P = .002), especially for the subgroup with cleft palate (0.52 vs 0.44 cases per 1000 live births; P = .006). Children with OFC, compared with children without OFC, were more likely to be born prematurely (406 children [13.3%] vs 1086 children [7.1%]; P < .001; standardized difference, 0.21) and had lower mean (SD) birth weight (3215.3 [687.6] g vs 3382.6 [580.0] g; P < .001; standardized difference, 0.26). The mortality rate among children with OFC was higher than among matched children without OFC (hazard ratio, 10.60; 95% CI, 7.79-14.44; P < .001). When mortality was adjusted for the presence of congenital or chromosomal anomalies, the risk of death was not significantly different between children with OFC and those without OFC (hazard ratio, 1.35; 95% CI, 0.73-2.72). Conclusions and Relevance: These findings suggest that incidence of OFC In Ontario, Canada, decreased from 1994 to 2017. Mortality in children with OFC was high, especially in the first 2 years of life, and was predominantly associated with the presence of other congenital or chromosomal anomalies. Further research is required to better understand the causes of wide geographical variations of OFC incidence and improve the survival of these patients.

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)0.001
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 score0.027
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.016
GPT teacher head0.248
Teacher spread0.232 · 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

Citations31
Published2020
Admission routes3
Has abstractyes

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