Bibliographic record
Abstract
If there had been a simple early clinical test for urine nitrogen or plasma amino acids, the diseases we know as “diabetes mellitus” might instead have been called “diabetes proteinus.” This is because insufficient insulin action impacts all macronutrients, not just carbohydrate. With our present preoccupations with “glycemic control” and the proven risks associated with hyperglycemia and hyperlipidemia, clinicians may indeed be underestimating the importance of altered protein metabolism and of the quality and quantity of dietary protein. An exception to this is diabetic nephropathy, addressed by Wheeler et al. (1) in this issue of Diabetes Care . Uncontrolled diabetes has been recognized for millennia to cause lean tissue loss. Though anticatabolic and anabolic actions of insulin are accepted as dogma, relatively little clinical research is conducted on them. In the present millennium, publications in the American Diabetes Association (ADA) journals have cited “glucose metabolism” 389 times, “lipid metabolism” 196 times, but “protein metabolism” only 30 times! Protein metabolism is not less than one-tenth as important as glucose. For example, though we use HbA1c as indicator of recent glycemic control, the steady state of glycation of each protein is also a function of the rate of turnover of that protein. The first paper of this millennium in Diabetes Care (2) showed increased whole-body fed-fasted protein turnover in the presence of hyperglycemia; hence, the kinetics of many proteins are increased. The therapeutic implication of recognizing that protein metabolism is disturbed in diabetes is that dietary protein quality and quantity need to be adapted to individual requirements. Many of the controversies over dietary protein recommendations have been summarized recently by Franz (3). The most recent ADA Clinical Practice Recommendations (4) and corresponding technical papers (5) appeared in Diabetes Care in January 2002. Therein, the level of evidence for establishing recommendations was …
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 0.015 |
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".