Genetic variation and health; towards individualized medicine.
Bibliographic record
Abstract
BACKGROUND: Pediatric endocrine diseases and their adult consequences depend on interactions of environmental exposure factors with the genetic makeup of the individual. Much has been learned over the past two decades about the genetic component in cases of monogenic (Mendelian) disorders due to drastic disruption of a single gene. The majority of children consulting a pediatric endocrinologist, however, suffer from conditions not attributable to a single gene. The nature and mechanisms of more subtle alterations at multiple different genes that are responsible for the genetic component of most human morbidity and mortality is now only beginning to be elucidated, largely thanks to the vast amount of information that is becoming available through the human genome effort. OVERVIEW: The purpose of this review is to describe 1. the nature of the information on health-related human variation that is coming our of the genome effort; 2. how this variation can affect biology; 3. which research approaches show promise in linking DNA variation to disease; 4. how this variation is organized in genomic blocks of linkage disequilibrium; and 5. how this knowledge may, in the future, allow clinicians to individualize management of a specific patient according to his/her genetic makeup.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".