Bibliographic record
Abstract
At least 8% of the combined population of the United States (US) and Canada have diabetes, and in about one-third of these people, the disease is undiagnosed [1]. Diabetes is not evenly distributed geographically, and there is surprisingly little consensus among categories of medical providers as to when and how intensive insulin therapy should be initiated. Although some physicians worry that insulin therapy may promote insulin resistance or increase the risk of cardiovascular events, the best current clinical evidence suggests that such fears are largely unfounded. Similarly, new evidence shows that the weight gain associated with insulin therapy is by no means always dramatic or progressive. The view that insulin is not effective in type 2 diabetes, although common in the US, is not shared by physicians experienced and skillful in its use and is refuted by recent clinical studies. Promising new patterns of insulin use in type 2 patients are emerging in the US: the availability of insulin as a single dose, rather than moving directly to multiple daily injections; the practice of continuing rather than stopping oral agents when an evening insulin dose is added; and the use of new insulins and insulin-sensitizing agents that facilitate therapy and increase its effectiveness. Several new treatment options are discussed.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".