Bibliographic record
Abstract
Failure of the pancreatic beta cells to produce insulin or development of defective molecular signaling of insulin to the peripheral tissue cells (insulin resistance) induces persistent hyperglycemia and accumulation of fatty acids in the blood of patients with diabetes. Over time, those changes lead to microvascular and macrovascular damage in various target organs. In patients on peritoneal dialysis (PD), complications may accelerate with treatment using conventional glucose-containing solutions. Strategies for proper glycemic control in diabetic PD patients are therefore essential to prevent complications and to maintain a good quality of life. Dietary restrictions and weight control remain the foundation of the management approach for glycemic control. Further therapeutic actions include the stepwise addition of oral hypoglycemic agents and insulin, based on individual assessment of PD patients. Other strategies of immediate importance in reducing hyperglycemia are to use PD exchanges with new non glucose PD solutions (such as those with icodextrin or amino acids) in combination with fewer daily exchanges of low-glucose solutions. Combined, these approaches will sufficiently control hyperglycemia in diabetic PD patients. Research is in progress to develop therapeutic agents aimed at correcting various molecular defects of insulin signaling or at reducing protein kinase C activation induced by oxidative stresses in various tissue cells. Clinical experience with the use of such agents in diabetic PD patients is limited at present.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".