Maturity Onset Diabetes of the Young – An Overview of Common Types. A Review
Bibliographic record
Abstract
Abstract Background and aims: Maturity Onset diabetes in Young (MODY) is an autosomal dominant disease and according to an estimate, the MODY cases are 2% of all the diabetic cases. The objective was to review the common types of MODY reported in literature in context to their geographical areas. Material and method: For literature search, PubMed data base was used. The key word was “Maturity onset diabetes of the young”. The articles were reviewed by titles and if found relevant, the abstract and full article (if available) were retrieved. The studies that were published in English, presented original data and describe type of MODY were included. The information related to author, year of publication and type of MODY was extracted in excel sheet. Results: A total of 1135 studies resulted which were reviewed, and 206 articles were finally selected. The studies were grouped according to the regions i.e. Asia, Europe, America, Africa and Australia & Oceania. The MODY 2 was most prevalent in regions i.e. Asia, Europe, America and Australia & Oceania followed by MODY 3. When analysed according to countries, MODY 2 was found prevalent in India, Korea, UK, Italy, Spain, Czech Republic, Canada and Brazil while MODY 3 was common in Japan, China, France Norway and Germany. Conclusion: The MODY 2 was most common. The data from south Asian countries including Pakistan is lacking. As there is a huge burden of diabetes in the country so there is a dire need to do large scale studies on MODY in the country.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".