Bibliographic record
Abstract
The clinical utility of genetic tests is determined by the outcomes following test use. Like other measures of value, it is often contested. Stakeholders may have different views about benefits and risks and about the importance of social versus health outcomes. They also commonly disagree about the evidence needed to determine whether a test is effective in achieving a specific outcome. Questions may be presented as factual disagreements, when they are actually debates about what information matters or how facts should be interpreted and used in clinical decision-making. Defining the different issues at stake is therefore an important element of policy-making. Key issues include evidence standards for test use, and in particular, the circumstances under which prospective controlled data should be required, as well as evidence on feasibility, cost and equitable delivery of testing; the goals of population-based screening programs, and in particular, the role of social outcomes in evaluating test value; and the appropriate uses and funding of tests that inform non-medical actions. Addressing each of these issues requires attention to stakeholder values and methods for effective deliberation that incorporate consumer as well as health professional perspectives.
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 | 0.305 | 0.495 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.069 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.017 | 0.026 |
| Insufficient payload (model declined to judge) | 0.008 | 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".