Bibliographic record
Abstract
Intervention trials in type Ⅰ diabetes NOD mice models have shown effective in preventing and reversing of diabetes.Finished trials in human seem less effective,even the French and Canadian cyclosporine A intervention trials resulted in increased remission rate and 1-4 year effect in slowing β cell function deterioration than placebo.But concerns on the cyclosporine A nephrotoxicity ended its further efforts on its way to a favorable intervention agent for type Ⅰ diabetes.Currently there are hundreds of phase I and II clinical trials for study of possible effective agent on newly diagnosed type Ⅰ diabetes intervention.Agents for intervention are generally classified according to their antigen-specificity(antigen-specific and nonantigen-specific) and β cell growth or anti-apoptotic agents.Combination of these agents from different class would be safer and more effective in reversing turmoil of β cell autoimmunity.Tertiary prevention trial would be more practical based on our current knowledge of type Ⅰ diabetes.More accurate methods for prediction of disease onset and of intervention efficacy evaluation are urgently needed.
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 | 0.022 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".