Bibliographic record
Abstract
There is an epidemic of Type 2 diabetes. It is predicted that by 2030 that the number of those with Type 2 diabetes will rise to over 350 million globally. There has been much debate regarding the cost effectiveness of early intervention in Type 2 diabetes. Studies have shown that despite ' intensively' treated patients serious complications still occur. Research indicates there is demonstrable and quantifiable insulin resistance and either impaired fasting glucose or impaired glucose tolerance for 5 to 10 years before the onset of Type 2 diabetes. It is in this state of a glucose metabolism disorder there is an opportunity to prevent or delay the onset of diabetes. This project provides a comprehensive review of the contemporary research literature to answer the question What is the best test to screen adults for glucose metabolism disorders, for earliest possible detection in a primary care setting in Canada . A review of 71 studies identified strengths and weaknesses of a variety of screening modalities. The key finding was that the Oral Glucose Tolerance test (OGTT) is the most sensitive test in all populations for identification of glucose metabolism disorders. It is also clear that despite recommendations by contemporary clinical guidelines, the use of HbA1c is ill suited to identification of glucose metabolism disorders. It is therefore recommended that the OGTT be used were feasible in testing for glucose metabolism disorders, preferably with plasma insulin levels being done at the same time to facilitate the use of an insulinogenic index. If this is not possible, consideration should be given for using glucose challenge testing or Hba1c and fasting plasma glucose in combination. The Nurse Practitioner, along with other primary health care practitioners, is in an ideal position to intervene in the disease trajectory through effective screening and patient education. Interventions have the promise of improving patient and health system outcomes and reducing the future personal and financial
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".