A Maturity Level Model (MLM) for the self-assessment of genomic medicine practices in healthcare systems
Bibliographic record
Abstract
Genomic medicine implementation in healthcare systems can bring us one step closer to making personalised medicine a reality, with major socioeconomic benefits. Citizens and patients can widely benefit from genomic data analysis for accurate and timely diagnosis, effective treatments with less adverse events, and accurate profiling for disease prevention. Implementation of genomics in healthcare is complex and requires adjustments in the governance, structure and organization of health services, as well as dedicated investments. Implementation is also dependent on the country context. In the context of the 1+Million Genomes (1+MG) initiative, we developed a Maturity Level Model (MLM) for health systems to self-evaluate the maturity of their genomic medicine practices, and define a path to optimization. MLM is a tool for healthcare systems to self-evaluate the level of maturity of their genomic medicine practices according to a common matrix, and to define a path to optimization. A MLM pilot in eight European countries provided important information regarding common strengths, weaknesses and asymmetries across Europe.
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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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".