Bibliographic record
Abstract
The incidence of type 1 diabetes in Latin America ranges from 0.4 to 8.3 cases per 100000 children under 15 years of age, and the prevalence of type 2 diabetes ranges from 1.2% to 8%, with higher prevalence rates in urban areas. The frequency of diabetes in Latin America is expected to increase by 38% over the next 10 years, compared with an estimated 14% increase in the total population. The total number of cases of diabetes is expected to more than double and to exceed the number of cases in the US, Canada, and Europe by 2025. Factors underlying this increase include aging and increased life expectancy of the population, increased urbanization, and lifestyle changes among Native American populations. In many places, only a minority of individuals currently receives treatment for diabetes. Furthermore, the diagnosis of type 2 diabetes often occurs late in the course of the disease, with the result that 10-40% of patients have chronic complications at the time of diagnosis. Hospital costs account for most direct expenditures associated with treatment, and mortality associated with diabetes has increased markedly in some areas over the past 2 decades.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".