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Record W4296498818 · doi:10.1093/pch/21.supp5.e58

Increasing Incidence of Optic Nerve Hypoplasia/ Septo-Optic Dysplasia Spectrum: Geographic Clustering in Northern Canada

2016· article· en· W4296498818 on OpenAlexaffabout
C Rodd, T Khaper, M Bunge, I Clark, M Rafay, A Mhanni, N Kirouac, Ambika Sharma, Brandy Wicklow

Bibliographic record

VenuePaediatrics & Child Health · 2016
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsResearch Manitoba
Fundersnot available
KeywordsOptic nerve hypoplasiaMedicineIncidence (geometry)Optic nerveHypoplasiaPediatricsDysplasiaOphthalmologyDemographyPathologySurgery

Abstract

fetched live from OpenAlex

Abstract BACKGROUND: Since optic nerves and pituitary gland are embryologi-cally related, optic nerve hypoplasia (ONH) and septo-optic dysplasia (SOD) represent a clinical spectrum associated with visual impairment, pituitary deficiencies, and severe CNS structural malformations(SODplus). ONH is a leading cause of pediatric blindness in North America; genetic mutations are rarely observed. We recently perceived an increase in the number of children with SOD in our clinic. Similarly, several studies have reported a rise in the incidence of ONH/SOD in other jurisdictions. OBJECTIVES: Our primary objectives were to examine trends in annual incidence in Manitoba and geographical clustering in our catchment area ofManitoba, NW Ontario and Nunavut. DESIGN/METHODS: This was a retrospective 1996-2015 chart review from all medical services (Neurology, Ophthalmology, Endocrinology, Genetics) caring for these children to extract information pertaining to anthropometric measures, radiologic findings, parental characteristics, endocrinopathies, and neurologic symptoms. Postal codes were used to assign map co-ordinates and census-based material and social deprivation indices. Numbers of children from Manitoba only were used to calculate annual incidence. From 2010-2014, a Quality Assurance (QA) sub-study identified all pediatric radiology reports containing the words 'optic nerve'; the additional cases of ONH/SOD children not identified by chart review were used to better define the true incidence in Manitoba. RESULTS: Ninety-three children were identified in our catchment area by chart review; Poisson regression confirmed a striking 1.11-fold annual increase (95%CI=1.07-1.16) or ~800% over two decades. The annual incidence (averaged 2010-2014) reached 53.3 per 100,000 affecting 1 in 1875 live births (chartdata). Including children identified by QA sub-study, the incidence rose to 113.3 per 100,000 live births in Manitoba. These are much higher than previously reported. Most children (~60%) had SODplus. Common presenting or follow-upfeatures were hypoglyce-mia, nystagmus, seizures, and developmental delay (50%); 40% had hormone deficiencies; 80% (75/93) had reduced visual acuity, typically bilateral. Many childrenwere born prematurelywith young (mean 21y: IQR 19-26y), primiparous mothers. Unhealthy maternal lifestyles and severe material deprivation were noted. There wasdisproportionate clustering in Northern Manitoba (3 times the average provincial rate) and in Nunavut. CONCLUSION: We noted a dramatic rise in the annual incidence of ONH/SOD in Manitoba, NW Ontario and Nunavut, which is much higher than previous reports. This disorderwas strongly associated with poverty in northern communities. The temporal picture was consistent with environmental, nutritional, or toxic etiologies. About half of the children were severely affected with increased morbidity and health care burdens.

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)0.001
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 score0.023
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.015
GPT teacher head0.288
Teacher spread0.273 · 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

Citations0
Published2016
Admission routes2
Has abstractyes

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