MétaCan
Menu
Back to cohort
Record W2007724248 · doi:10.1080/02713680490894315

<i>In Vivo</i>Assessment of Postnatal Murine Ocular Development by Ultrasound Biomicroscopy

2005· article· en· W2007724248 on OpenAlexaff
Allison S. Brown, Mingyu Zhang, Viviene Cucevic, Charles J. Pavlin, F. Stuart Foster

Bibliographic record

VenueCurrent Eye Research · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnexins and lens biology
Canadian institutionsHospital for Sick ChildrenWomen's College HospitalUniversity of TorontoHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsUltrasound biomicroscopyIn vivoOphthalmologyUltrasoundMedicineLens (geology)PathologyBiologyAnatomyRadiologyGlaucoma

Abstract

fetched live from OpenAlex

PURPOSE: Ultrasound biomicroscopy (UBM) can noninvasively provide anatomical information about mouse ocular structures. We present the quantitation of postnatal murine eye development using UBM. MATERIALS AND METHODS: The eyes from CD-1 mice were examined at 1, 2, 4, 6, and 8 weeks of postnatal development using 40 MHz UBM. Patterns of ocular tissue growth including the lens, globe, and anterior chamber were calculated. RESULTS: Postnatal CD-1 lens and globe volumes are consistent with an exponential decay of growth during the first 8 postnatal weeks. Anterior chamber depth increases most sharply in the first 2 postnatal weeks but continues to increase up to the 8th postnatal week. Anterior segment angle was observed to increase from 1 to 4 weeks. CONCLUSIONS: UBM can be used to obtain in vivo quantitative measurements of postnatal murine ocular structures. Our ability to obtain ocular anatomical information will facilitate future assessments of mouse models of human disease.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.029
GPT teacher head0.417
Teacher spread0.389 · 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 designBench or experimental
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

Citations19
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Eye ResearchSame topicConnexins and lens biologyFrench-language works237,207