Bibliographic record
Abstract
As one considers the near future of diabetes in children, some very sobering thoughts are in order. First, with respect to the magnitude of the problem, the worst is yet to come! The emergence of new economic giants such as China and India will bring with it potential massive increases in type 1 diabetes (2-5% per annum increases in incidence in the world's most populous countries) and childhood obesity (with its associated insulin resistance and type 2 diabetes). This will demand the training of enormous numbers of health care professionals and delivery of insulin and testing equipment that is of high quality and reasonable cost. Second, the majority of children with diabetes presently do not and, likely in the foreseeable future, will not achieve and maintain levels of metabolic control that provide protection from microvascular and macrovascular complications. In fact, an analysis by the Centers for Disease Control in the USA suggested that a child of 10 yr of age developing diabetes in the year 2000 would live a further approximately 50-55 yr, losing about 18-20 yr of life, this in the world's richest nation!Finally, Edwin Gale in 2005 issued the following warning: 'The individual and communal legacy of poor glucose control will remain with us for the next 30 years, EVEN if an effective means of preventing new cases of the disease were to be introduced tomorrow'.There is much work to be done and little room for complacency. Only when every single child with diabetes has ready access to experienced health care professionals, insulin, and other supplies, food water and protection will the first part of the job be done.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".