Delivery models for predictive genetic testing: preliminary results of a systematic review
Bibliographic record
Abstract
Background Research on the integration of genomic knowledge into clinical practice and public health is in an early phase, and many concerns remain. The aim of this study is to identify, classify, and evaluate delivery models for the provision of predictive genetic testing in Europe vs. extra-European (Anglophone) countries. Methods A systematic review of the literature was conducted to identify existing genetic delivery models. Inclusion criteria were that articles be: published 2000-2015; in English or Italian; and from European or non-European countries (Canada, USA, Australia or New Zealand). Additional policy documents were retrieved from represented countries’ government-affiliated websites (non-systematic search). Results A total of 117 records were included, reporting on 148 genetic programmes. The programmes integrated into healthcare systems were 99 (64.9%), 49 (33.1%) were pilot programmes and 4 (2.7%) were direct-to-consumer genetic services. Most programmes were delivered in the United Kingdom (58, 39.2%), USA (35, 23.6%) or Australia (16, 10.8%). Tests for hereditary breast and ovarian cancer and Lynch syndrome were most commonly offered (39.9% and 12.8% of programmes, respectively). Many of the genetic tests offered have insufficient clinical validity or utility. The identified genetic programmes can be classified into five basic genetic service models based on which type of healthcare professional has the most prominent role in test referral: I) the geneticists model; II) the primary care model; III) the medical specialists model; IV) the population screening programmes model; V) and the direct-to-consumer model. Rudimentary evaluation of the identified programmes will be made based on outcomes and process measures of the models. Conclusions This review, as part of an European multicenter study, will facilitate the identification of appropriate models, outcome and process measures for the provision of predictive genetic testing in Europe. Key messages: Current genetic services are delivered without standardized set of process and outcome measures, which are essential for the evaluation of healthcare services Identification of appropriate genetic services delivery models is important for the implementation of genetic applications of proven efficacy, effectiveness and cost-effectiveness
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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 | 0.062 | 0.211 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.015 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".