Development and description of GETT: a Genetic testing Evidence Tracking Tool
Bibliographic record
Abstract
BACKGROUND: The completion of the Human Genome Project has increased the pace of discovery of genetic markers for disease. Despite tremendous efforts in fundamental research, clinical applications still lag behind expectations, partly due to the lack of effective tools to systematically search for and summarize published data relative to the clinical assessment of new diagnostic molecular tests. METHODS: Through a collaborative process using published tools and an expert panel, we developed a detailed checklist of the evidence that needs to be collected or produced to evaluate the potential usefulness of a new molecular diagnostic test. This tool is called GETT, for Genetic testing Evidence Tracking Tool. RESULTS: GETT allows 1) researchers to summarize the current evidence and to identify knowledge gaps for further research and; 2) stakeholders to collect data related to a given molecular test and improve their decision-making process. GETT comprises 72 clearly defined items/questions, grouped into 10 categories and 26 sub-themes, including an overview of disease epidemiology and genetics, the available diagnostic tools, and their analytical and clinical performances, availability of quality control programs, laboratory and clinical best practice guidelines, clinical utility, and impact on health care and psycho-social, ethical and legal implications. It also includes a summary of the evidence available and attempts to prioritise knowledge gaps related to the testing. We also compare GETT to other existing frameworks. CONCLUSIONS: This systematic evidence-based tracking tool, which is more detailed than existing frameworks and provides clear definition for each item, will help streamline collection of the available evidence to appraise the potential for clinical application of new molecular diagnostic tests and prioritize research to produce the evidence-base relative to the clinical implementation of molecular diagnostic tests.
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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 | 0.108 | 0.319 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.027 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.036 | 0.009 |
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".