Iran Diabetes Research Roadmap (IDRR) Study; Knowledge Gap in Ge-netic Research on Diabetes Mellitus in Iran: A Review Article
Bibliographic record
Abstract
Background: Diabetes mellitus (DM) is the most common metabolic disorder worldwide. This study aimed to report characteristics of genetic studies in DM in Iran and to identify knowledge gap in genetics of diabetes in Iran. Methods: All publications of Iranian authors in national and international journals up to 2015 were included. Comprehensive search was performed in PubMed, Web of Science, Scopus, SID, IranMedex and Magiran using "Diabetes mellitus" and "Iran*" keywords and their combination. This search obtained 25589 documents. The obtained documents were categorized into eleven groups of complications, comorbidity, management, psychology, nutrition, physical activity, genetics, basic sciences, prevention, education and gestational diabetes mellitus (GDM). Documents were categorized based on publication year, WHO and Australian National Health and Medical Research Council (NHMRC) classification, study design and subject area. Results: After screening, 293 documents remained. The trend of publications was increasing and reached peak in 2013. Case-control was the most common method used in the documents. Most of the studies were association study with case-control design while there was no genome-wide association study (GWAS). Genetic risk factors for DM and its complications were the most common topics in the obtained documents followed by DM management. Conclusion: The most of genetic studies in diabetes in Iran are association studies about genetic risk factors of diabetes while GWAS and pharmacogenetic studies are rare or absent. This may indicate low priority of personalized medicine in the field of diabetes in Iran.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".