Genetics in Health Care: An Overview of Current and Emerging Models
Bibliographic record
Abstract
BACKGROUND: With advances in genetic and genomic medicine, the optimal integration of genetic services into the health care system remains of major concern in many countries. OBJECTIVES: To review the current organisation of genetic services, mostly in Europe, North America and Australia, explore emerging service delivery models, and probe challenges inherent in the transition process. METHODS: We conducted a literature review of genetics in clinical practice: testing, diagnosis, counselling, and treatment. We examined the basic structures of genetic services, examples of integrated networks, and existing professional resources. We investigated services belonging traditionally in medical genetics as well as those developed for more common diseases. RESULTS: Multidisciplinary specialist clinics and coordinated services appeared to be key to delivering proper care in rare genetic disorders. For oncogenetics, neurogenetics and cardiogenetics, interprofessional collaboration between geneticists and other specialists seemed to be favoured. On the other hand, there was also a tendency toward the integration of genetic services directly into primary care. Among the most pressing challenges was the morphing of paediatric care into adult care. CONCLUSION: The coordination of activities between professionals in first-, second-, and third-line medical care is a primary objective calling for the reconfiguration of professional roles and responsibilities. This entails the forging of new relationships as well as an enhanced sharing of expertise and genetic information, including information regarding services. Barriers to overcome include the redistribution of roles, sharing of data and databases, and the lack of preparedness of non-genetics professionals and of the health care system in general.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".