Farmakogenetik Testlerin Sağlık Ekonomisi Alanındaki Etkisinin Bibliyometrik Analizi
Bibliographic record
Abstract
The aim of this study is to examine scientific publications addressing the impact of pharmacogenetic testing in the field of health economics using bibliometric methods, thereby revealing the structure of the field, its collaboration networks, and its thematic focuses. Studies in which the terms ‘pharmacogenetic/pharmacogenomic testing’ and "economic evaluation, cost-effectiveness, cost-utility, cost-benefit, cost-minimisation, health economics, health technology assessment‘ in the title, abstract, and keyword fields, and limited to document types “article” and ’review". The 1,297 studies meeting the inclusion criteria were analysed using VOSviewer. Author collaboration networks, country citations, keyword co-occurrence and citation analyses were conducted. In addition, a sub-analysis was performed on 42 articles that directly emphasised pharmacogenetics and economic evaluation according to the title search criteria. The findings showed that publications were largely concentrated in the United States, the United Kingdom, the Netherlands, Canada, and other high-income countries, with researchers such as Munir Pirmohamed, George P. Patrinos, and David I. Veenstra at the centre of the network. Within the keyword network, the concepts of pharmacogenetics/pharmacogenomics, personalised medicine, and cost-effectiveness were found to cluster particularly around classic drug-gene pairs such as warfarin–CYP2C9 and clopidogrel–CYP2C19. The title-focused sub-analysis revealed that studies conducting direct economic evaluations are limited in number but highly visible in terms of citations. The results indicate that the evidence base regarding the economic value of pharmacogenetic testing is developing but remains limited, with significant gaps existing in the context of low- and middle-income countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.034 | 0.038 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".