MétaCan
Menu
Back to cohort
Record W2161448406 · doi:10.1017/s0952523806231122

Expansion of visual receptive fields in experimental glaucoma

2006· article· en· W2161448406 on OpenAlexaff
Wayne M. King, Vimal Sarup, Yves Sauvé, Colleen M. Moreland, David O. Carpenter, S.C. Sharma

Bibliographic record

VenueVisual Neuroscience · 2006
Typearticle
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsUniversity of Alberta
FundersNational Eye Institute
KeywordsReceptive fieldSuperior colliculusGlaucomaRetinaOphthalmologyIntraocular pressureRetinalVisual fieldGanglionRetinal ganglion cellBiologyMedicineAnatomyNeuroscience

Abstract

fetched live from OpenAlex

Glaucoma is a major cause of blindness and is characterized by death of retinal ganglion cells. In a rat model of glaucoma in which intraocular pressure is raised by cautery of episcleral veins, the somata and dendritic arbors of surviving retinal ganglion cells expand. To assess physiological consequences of this change, we have measured visual receptive-field size in a primary retinal target, the superior colliculus. Using multiunit recording, receptive-field sizes were measured for glaucomatous eyes and compared to both those measured for contralateral control eyes and to homolateral eyes of unoperated animals. Episcleral vein occlusion increased intraocular pressure. This was accompanied by a significant increase in receptive-field size across the superior colliculus. The expansion of receptive fields was proportional to both degree and duration of the increase of intraocular pressure. We suggest that this increase in the size of receptive fields of glaucomatous eyes may be related to the increase in the size of dendritic arbors of the surviving ganglion cells in retina.

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)0.001
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.314
Teacher spread0.300 · 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

Citations35
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueVisual NeuroscienceSame topicGlaucoma and retinal disordersFrench-language works237,207