Bibliographic record
Abstract
OBJECTIVE: To provide a primer for primary care professionals who are increasingly called upon to discuss the growing number of genetic screening services available and to help patients make informed decisions about whether to participate in genetic screening, how to interpret results, and which interventions are most appropriate. QUALITY OF EVIDENCE: As part of a larger research program, a wide literature relating to genetic screening was reviewed. PubMed and Internet searches were conducted using broad search terms. Effort was also made to identify the gray literature. MAIN MESSAGE: Genetic screening is a type of public health program that is systematically offered to a specified population of asymptomatic individuals with the aim of providing those identified as high risk with prevention, early treatment, or reproductive options. Ensuring an added benefit from screening, as compared with standard clinical care, and preventing unintended harms, such as undue anxiety or stigmatization, depends on the design and implementation of screening programs, including the recruitment methods, education and counseling provided, timing of screening, predictive value of tests, interventions available, and presence of oversight mechanisms and safeguards. There is therefore growing apprehension that economic interests might lead to a market-driven approach to introducing and expanding screening before program effectiveness, acceptability, and feasibility have been demonstrated. As with any medical intervention, there is a moral imperative for genetic screening to do more good than harm, not only from the perspective of individuals and families, but also for the target population and society as a whole. CONCLUSION: Primary care professionals have an important role to play in helping their patients navigate the rapidly changing terrain of genetic screening services by informing them about the benefits and risks of new genetic and genomic technologies and empowering them to make more informed choices.
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 | 0.027 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.021 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.020 | 0.025 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".