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Record W2417560804

Normal distribution of palpebral fissure lengths in Canadian school age children.

2010· article· en· W2417560804 on OpenAlexaboutno aff
Sterling K. Clarren, Albert E. Chudley, Louis Ngai Yuen Wong, Janis Friesen, Rollin Brant

Bibliographic record

VenuePubMed · 2010
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsnot available
Fundersnot available
KeywordsPalpebral fissureNormativeDemographyMedicineEthnic groupCensusPopulationDiversity (politics)GerontologyFamily medicineEnvironmental healthSurgery
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Fetal alcohol syndrome (FAS) includes the facial dysmorphic feature of short palpebral fissures (PFs) and short PFs are a key physical marker for identifying children with FAS and some other rarer conditions. There is concern that normative data on PFs now available may not reflect all racial/ethnic groups and might be inaccurate in general. OBJECTIVES: To accomplish a large population based study that would accurately determine normative PF values across the full diversity of the Canadian school age population. METHODS: A normative sample of school age children was identified in Vancouver, British Columbia and Winnipeg, Manitoba to reflect the diversity of racial and national groups in Canada. The sample included students in grades 2, 4, 6, 8, and 10 from 17 schools in Vancouver and 31 schools in Winnipeg. Schools were selected based on racial diversity obtained from data from the 2001 Statistics Canada census. 1064 students in Vancouver and 1033 students in Winnipeg were photographed in a standardized way. Photographs were analyzed using a computerized method. RESULTS: Analysis demonstrated that PFs do grow with age and there is a slight but meaningful difference between boys and girls in each age group. It is possible to define Canadian standards without reference to racial or ethnic origin. CONCLUSION: Mean results with norms and standard deviations are presented in figures for clinical use and are clinically smaller than those found in the most commonly used reference book.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.005
GPT teacher head0.200
Teacher spread0.195 · 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

Citations43
Published2010
Admission routes1
Has abstractyes

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