Bibliographic record
Abstract
Abstract Most genetically determined differences between individuals, including (but not limited to) susceptibility to specific diseases, depend on functional variation at multiple genes (loci). The frequency of these variants (alleles) in the general population varies from common to rare. The contribution of most common alleles, each in isolation, to disease risk is weak. Most uncommon and rare alleles have not been studied to date and some may have strong effects on disease risk even if they contribute to disease cause each in a small subset of cases. This mixture of strong and weak effects by common and rare alleles is referred to as allelic architecture. Defining the allelic architecture of each disease will be the first step towards using an individual's genetic profile to individualise the molecular diagnosis within a group of cases that all bear the same clinical diagnostic label. Key Concepts: Most diseases and other human traits have statistically significant familial clustering, indicating the involvement of genetic susceptibility. Genetic susceptibility to most diseases is determined by a large number of gene variants (loci). Disease‐associated variants may change the sequence of the protein product, or the control of its transcription, RNA stability or translational efficiency. Most known loci have a weak genetic association with the disease or trait. A genetic association may be weak because of weak biology, or because the associated allele is merely an imperfect marker for an untested rare allele with strong effect. The discovery, among low‐frequency alleles, of those that have the strongest effects, is the next frontier in the genetics of complex traits and the greatest promise to personalised medicine.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".