Bibliographic record
Abstract
Abstract Recent advances in genome‐wide genotyping together with new technologies provide unprecedented opportunities for multiplex screening. These advances provide insight into diseases, hold promises to improve clinical practices to address lifestyle changes, inform reproductive decisions, identify newborns at risk as well as possibly move genetic screening out of the realm of the clinics into direct‐to‐consumer market forces. Advances in multiplex genetic screening raise ethical issues with regard to consent. Some of the concerns that may arise include how to: manage the incidental and excess information; integrate information about susceptibility testing into the clinic, given the complexity of the information; address the psychosocial impact and educate health professionals about the meaning of the results. If multiplex screening is used in genomic research and made available in the clinic, each ethical issue deserves consideration in the consent process and should be discussed. Key concepts: Become aware of new technologies that are being used to expand screening. Understand the distinction between testing and screening. Describe a spectrum of social, ethical issues that are involved in multiplex testing and screening. Know why multiplex screening/testing requires informed consent. Familiarize the reader with the concept of broad consent. Identify the emerging issues relevant to multiplex screening/testing. Consider whether multiplex screening/testing will shift the clinical approach towards a ‘direct‐to‐consumer’ model.
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 | 0.026 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".