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Myelinated retinal fibers in autosomal recessive spastic ataxia of Charlevoix-Saguenay

2011· article· en· W2151839197 on OpenAlexaboutno aff
Enzo Maria Vingolo, Roberto Di Fabio, S. Salvatore, Giulio Grieco, Enrico Bertini, Vincenzo Leuzzi, Claudia Nesti, Alessandro Filla, Alessandra Tessa, Francesco Pierelli, Filippo M. Santorelli, Carlo Casali

Bibliographic record

VenueEuropean Journal of Neurology · 2011
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsnot available
FundersMinistero della SaluteEuropean Commission
KeywordsRetinaMedicineRetinalOphthalmologyOphthalmoscopySpasticOptical coherence tomographyNeuroscienceCerebral palsyBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Myelinated retinal nerve fibers are considered a hallmark of autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) in French Canadian patients. The demonstration of a worldwide distribution of this disease, as well as the almost invariable presence of a normal retina on fundoscopy in cases outside Canada, suggests that more quantitative methodologies are needed to assess the retina in ARSACS. METHODS: To characterize better the retinal features of ARSACS, we studied five Italian patients by means of optical coherence tomography (OCT), a processing method that allows the creation of three-dimensional images with micrometer resolution. We compared OCT characteristics in ARSACS with those obtained from five subjects with persistent myelination of the retina, a rare congenital non-progressive anomaly. RESULTS: Four patients with ARSACS showed myelinated retinal nerve fibers on ophthalmoscopy, corresponding to an increased thickness of the retina on OCT, a characteristic not present in the subjects with persistent myelination of the retina. CONCLUSIONS: Myelinated retinal fibers are not rare in Italian patients with ARSACS. This finding may be the consequence of the thickening of the retina, as detected by OCT.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.419
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 integrity0.0000.001
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 head0.249
Teacher spread0.208 · 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 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

Citations36
Published2011
Admission routes1
Has abstractyes

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