Genetic epidemiology in age‐related cataract research
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".