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Record W3080802341 · doi:10.1186/s13005-022-00310-9

Orbital volume changes during growth and development in human children assessed using cone beam computed tomography

2022· article· en· W3080802341 on OpenAlexaff
Eric A. Smith, Caroline S. Halbach, Adriana Robertson, Aden M. Peterson, Andrew R. Harrison, Thorsten Grünheid, Brent E. Larson, Ali Mokhtarzadeh

Bibliographic record

VenueHead & Face Medicine · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicOcular Disorders and Treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineCone beam computed tomographyCraniofacialOrbit (dynamics)Craniofacial surgeryVolume (thermodynamics)Computed tomographyRetrospective cohort studyNuclear medicineOrthodonticsSurgery

Abstract

fetched live from OpenAlex

OBJECTIVES: To measure growth-related changes in orbital volume from childhood to the late teenage years using cone-beam computed tomography (CBCT) scans. METHODS: This retrospective cohort study involved 65 (24 male, 41 female) healthy Caucasian children (ages 6-18 years) with existing serial craniofacial CBCT scans. CBCT scans were available for 292 orbits. Each orbit was transformed into a closed space with well-defined boundaries, and orbital volume was measured using manual segmentation. A novel statistical analysis was applied to extract the maximum amount of longitudinal information from the data. Intra- and inter-operator correlation coefficients were calculated from replications performed on a random subset of 10% of the sample. RESULTS: Orbital volume increased at a rate of 1-2% annually until the late teenage years. Intra- and inter-operator agreement between repeated measurements were >90%. CONCLUSIONS: Orbital volume increases by 1-2% per year throughout childhood continuing until the late teenage years. This annual increase is large enough to be clinically relevant as it may lead to less-than-optimal long term surgical outcomes when reconstructive surgery for the pediatric anophthalmic socket is required.

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 score0.259
Threshold uncertainty score0.581

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 head0.012
GPT teacher head0.254
Teacher spread0.242 · 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

Citations14
Published2022
Admission routes1
Has abstractyes

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