A Systematic Review of Value Criteria for Next-Generation Sequencing/Comprehensive Genomic Profiling to Inform Value Framework Development
Bibliographic record
Abstract
OBJECTIVES: To comprehensively identify and map an exhaustive list of value criteria for the assessment of next-generation sequencing/comprehensive genomic profiling (NGS/CGP), to be used as an aid in decision making. METHODS: We conducted a systematic review to identify existing value frameworks (VFs) applicable to any type of healthcare technology. VFs and criteria were mapped to a previously published Latin American (LA) VF to harmonize definitions and identify additional criteria and or subcriteria. Based on this analysis, we extracted a comprehensive, evidence-based list of criteria and subcriteria to be considered in the design of a NGS/CGP VF. RESULTS: A total of 42 additional VFs were compared with the LA VF, 88% were developed in high-income countries, 30% targeted genomic testing, and 16% specifically targeted oncology. A total of 242 criteria and subcriteria were extracted; 227 (94%) were fully/partially included in the LA VF; and 15 (6%) were new. Clinical benefit and economic aspects were the most common criteria. VFs oriented to genomic testing showed significant overlap with other VFs. Considering all criteria and subcriteria, a total of 18 criteria and 36 individual subcriteria were identified. CONCLUSIONS: Our study provides an evidence-based set of criteria and subcriteria for healthcare decision making useful for NGS/CGP as well as other health technologies. The resulting list can be beneficial to inform decision making and will serve as a foundation to co-create a multistakeholder NGS/CGP VF that is aligned with the needs and values of health systems and could help to improve patient access to high-value technologies.
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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 | 0.067 | 0.285 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.045 | 0.032 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".