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Genetic epidemiology in age‐related cataract research

2008· article· en· W1971259108 on OpenAlexaff
Jochen Graw, N. Klopp, Thomas Illig, Gerhard Welzl, Rolf Holle, HE Wichmann, Christa Meisinger

Bibliographic record

VenueActa Ophthalmologica · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnexins and lens biology
Canadian institutionsInstitute of Health Economics
Fundersnot available
KeywordsCataractsSingle-nucleotide polymorphismGeneticsConfoundingProbandLogistic regressionBiologyCrystallinEtiologyGenePopulationMedicineMutationGenotypeInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Purpose Age‐related cataracts are the major cause of blindness worldwide. However, the contribution of genetics to their etiology is largely unknown. In contrast, the congenital and juvenile forms of cataracts are mainly caused by de‐novo or hereditary mutations leading to severe changes in the structure and/or function of the corresponding proteins – as it is obvious from the dominant mode of inheritance of most of the mutations. In addition to rare mutations, these cataract‐causing genes have also polymorphic sites in their regulatory and coding sequences (single nucleotide polymorphisms, SNPs); they might contribute to minor changes in the structure and/or function of the corresponding proteins. These alterations could be cataractogenic per se (in a mild form) or they might lead to an increased sensitivity of the proteins to environmental stress. Methods In a new population‐based study in Augsburg (Germany), which will be finished in summer 2008, ~3000 probands have been asked for cataracts; the answers are being validated and further specified by the treating ophthalmologists. Results 16 SNPs from known cataract causing genes (coding for crystallins, connexins and transcription factors) have been identified to be informative without violation of the Hardy‐Weinberg equilibrium. They will be tested with respect to their association with age‐related cataracts by logistic regression allowing for adjustment with respect to age, gender and other confounding effects. Conclusion The results will be presented and discussed.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.141
GPT teacher head0.379
Teacher spread0.238 · 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 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".

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Citations0
Published2008
Admission routes1
Has abstractyes

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