Identification of delivery models for the provision of genetic testing, policies governing the use of genomic applications and evaluation of genetic services: a multicentre study
Bibliographic record
Abstract
The provision of genetic services, along with research in the fields of genomics and genetics, has evolved in recent years to meet the increasing demand of consumers interested in prediction of genomic diseases and various inherited traits (e.g. ability in sports, nutrigenomics, ancestry, etc.). Consumer demand and commercial interests have paved the way for the premature introduction, in the public and private healthcare sectors, of genetic tests with insufficient data on analytical and clinical validity, as well as clinical utility. There is also lack or insufficient evidence of cost-effectiveness of several genetic applications already introduced in clinical and public health practice. These concerns contribute to the lack of evidence on what constitutes an optimal genetic service delivery model, defined as the broad context within the Public Health Genomics framework in which genetic services are offered to individuals and families with or at risk of genetic disorders. The aim of this dissertation is to identify existing genetic service delivery models, policies governing the use of genomic applications, and measures to evaluate genetic testing and related services in Europe and extra-European (Anglophone) countries (Canada, USA, Australia, or New Zealand). Two methodological approaches have been employed, a systematic review of the literature and a cross-sectional study addressing healthcare professionals with good knowledge and/or experience on the provision of BRCA1/2, Lynch syndrome, familial hypercholesterolemia, and inherited thrombophilia genetic testing, policies on genetic applications and evaluation of genetic services. The identification and evaluation of existing genetic service delivery models are important steps towards the enhancement and standardization of genetic service provision. Current models of genetic services require the integration of genetics in all medical specialties, collaboration among different healthcare professionals, and redistribution of professional roles. Prior to implementation in clinical and public health practice, genetic tests should be evaluated based on available efficacy and cost-effectiveness data and offered to the citizens as right to benefit from innovative healthcare. The proper implementation of genomics application in mainstream medicine can be achieved through professional education, training, adequate funding, public policies, and public awareness of the field of genomic medicine.
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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 | 0.129 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".