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Record W2059535953 · doi:10.1115/sbc2007-176096

Interactions Between Factors Influencing Optic Nerve Head Biomechanics

2007· article· en· W2059535953 on OpenAlexaff
Ian A. Sigal, John G. Flanagan, C. Ross Ethier

Bibliographic record

VenueASME 2007 Summer Bioengineering Conference · 2007
Typearticle
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsUniversity of WaterlooUniversity of Toronto
Fundersnot available
KeywordsOptic nerveGlaucomaIntraocular pressureElevated intraocular pressureBiomechanicsOphthalmologyRetinal ganglion cellMedicineOptic cup (embryology)LaminaBlindnessRetinaOptometryAnatomyNeuroscienceBiology

Abstract

fetched live from OpenAlex

Glaucoma is the second most common cause of blindness worldwide, and elevated intraocular pressure (IOP) is the primary risk factor for developing this disease. It has been postulated that IOP-induced mechanical strain on optic nerve head (ONH) glial cells leads to retinal ganglion cell damage and the consequent loss of vision in glaucoma. To better evaluate this theory it is important to understand the biomechanical environment within the ONH. Unfortunately it is very difficult to make measurements in the ONH, and it is particularly difficult to access the region in the ONH where the ganglion cells are thought to be injured, namely the lamina cribrosa. We have therefore developed models of the ONH and used the finite element method (FEM) to predict ONH mechanical response to changes in IOP [1].

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score1.000

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 head0.042
GPT teacher head0.308
Teacher spread0.266 · 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.

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

Citations5
Published2007
Admission routes1
Has abstractyes

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