Bibliographic record
Abstract
Will a genetic link to the risk of hypertension be found? Hypertension genomics have come a long way from the initial beginnings of the 2007 Wellcome Trust Case Control Consortium genome-wide association study (GWAS),1 which failed to identify genetic associations due to relatively small sample sizes. Now, 10 years on, GWASs have grown by several orders of magnitude, with study sizes typically surpassing a quarter of a million participants, identifying over 200 novel loci for blood pressure in white Europeans and extending to East Asians and African ancestry. Newer GWAS methodologies are taking this further. By assessing rare variants, the total known blood pressure-associated variants have increased, in particular, rare missense variants demonstrating larger effects (>1.5 mm Hg/allele).2 Custom genotyping microarrays designed to facilitate targeted, cost-effective follow-up of nominal associations for metabolic and cardiovascular traits have also identified novel blood pressure genetic loci, together with fine-mapping of previously known signals.3
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 | 0.033 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".