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Record W2055569445 · doi:10.1109/tbme.2010.2085000

An Automated Hirschberg Test for Infants

2010· article· en· W2055569445 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2010
Typearticle
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsKappaOptical axisPupilOpticsLimits of agreementMathematicsPhysicsMedicineNuclear medicineGeometry

Abstract

fetched live from OpenAlex

A novel automated method to measure eye misalignment in infants is presented. The method uses estimates of the Hirschberg ratio (HR) and angle Kappa (the angle between the visual and optical axis) for each infant to calculate the angle of eye misalignment. The HR and angle Kappa are estimated automatically from measurements of the direction of the optical axis and the coordinates of the center of the entrance pupil and corneal reflexes in each eye when infants look at a set of images that are presented sequentially on a computer monitor. The HR is determined by the slope of the line that describes the direction of the optical axis as a function of the distance between the center of the entrance pupil and the corneal reflexes. The peak of the distribution of possible angles Kappa during the image presentation determines the value of angle Kappa. Experiments with five infants showed that the 95% limits of agreement between repeated measurements of angle Kappa are ± 0.61 (°). The maximum error in the estimation of eye alignment in orthotropic infants was 0.9 (°) with 95% limits of agreement between repeated measurements of 0.75 (°).

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score0.545

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 science0.0010.000
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 head0.007
GPT teacher head0.255
Teacher spread0.247 · 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