The Role of Decision Impact Studies in Genomic Medicine in Cancer Care: A Scoping Review and Bibliometric Analysis Protocol
Bibliographic record
Abstract
Abstract BackgroundDecision impact studies have become increasingly prevalent in oncology in recent years, particularly in breast cancer prognostic research. Such studies, which aim to evaluate the impact of a test on clinical decision-making, appear to be a new form of knowledge with the potential to impact clinical practice and regulatory decision-making in genomic medicine. Yet their origins, intended purpose and usage have not yet been explored. The objectives of this review are to identify and characterize decision impact studies in genomic medicine in cancer care. This review is comprised of two parts. First, we will conduct a scoping review to catalogue the characteristics of decision impact studies. The scoping review will be followed by a bibliometric analysis to understand the role of actors and institutions in the production and dissemination of this new knowledge, by identifying influential articles, authors, global research trends and collaboration networks. MethodsWe will conduct a scoping review and a bibliometric analysis of the scoping review results. The search will include four databases, Medline, Embase, Scopus and Web of Science, using a comprehensive search strategy developed through a preliminary review of the literature. Arksey & O’Malley’s scoping review methodology, with updates by Levac et al. will be used, and the review will be reported following the PRISMA-ScR checklist. The FT Model will be used to collect and analyze data on clinical utility of decision impact studies. Our bibliometric analysis, using Bibliometrix software, will elucidate the evolution of these studies and provide data on the trends, influences and networks emerging in the field.DiscussionThis review will be a first step in understanding the evolution and uses of these studies and their potential influence on the integration of emerging genomic technologies into clinical practice. By exploring their origin and evolution across space and time, this study will equip future research to investigate the role of these studies in decision-making for regulatory processes, including market access and public and private coverage decision-making. Systematic review registration: Open Science Framework osf.io/hm3jr
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Bibliometrics Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.135 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.083 | 0.255 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".