Elevated Levels of Interleukin-6 in Young Adults With Type 1 Diabetes Without Clinical Evidence of Microvascular and Macrovascular Complications
Bibliographic record
Abstract
In the Presidential Address in June of 1998 (1), I proudly announced that the recently adopted Five Year Plan of the American Diabetes Association (ADA) contained the goal of allocating one of every three dollars of total public support to research awards and grants by the end of five years.I pledged to keep the members of the Professional Section apprised of our progress toward that goal.The general approach envisaged gradual increases in funding during the first three years, with more steep increases during the final two years.During the baseline fiscal year (FY) of 1998, before the initiation of the Five Year Plan, total public support was 90.8 million dollars, of which 15.5 million dollars, or 17.1%, was devoted to research awards and grants.During FY 1999, the first year of the Five Year Plan, total public support was 101.6 million dollars, of which 18.2 million dollars, or 17.9%, went to research awards and grants.During the past year, FY 2000, the second year of the Five Year Plan, total public support was 117.8 million dollars, of which 22.4 million dollars, or 19.0%, was allocated to research awards and grants.Although the largest increases are due in the final two years, we still have a long way to go.Members of the Professional Section need to use their influence (with both their patients and the ADA) to ensure that this ambitious goal is reached.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".