Revisiting the Pattern of Loss of β-Cell Function in Preclinical Type 1 Diabetes
Bibliographic record
Abstract
Type 1 diabetes (T1D) results from β-cell destruction due to autoimmunity. It has been proposed that β-cell loss is relatively quiescent in the early years after seroconversion to islet antibody positivity (stage 1), with accelerated β-cell loss only developing around 6-18 months prior to clinical diagnosis. This construct implies that immunointervention in this early stage will be of little benefit, since there is little disease activity to modulate. Here, we argue that the apparent lack of progression in early-stage disease may be an artifact of the modality of assessment used. When substantial β-cell function remains, the standard assessment, the oral glucose tolerance test, represents a submaximal stimulus and underestimates the residual function. In contrast, around the time of diagnosis, glucotoxicity exerts a deleterious effect on insulin secretion, giving the impression of disease acceleration. Once glucotoxicity is relieved by insulin therapy, β-cell function partially recovers (the honeymoon effect). However, evidence from recent trials suggests that glucose control has little effect on the underlying disease process. We therefore hypothesize that the autoimmune destruction of β-cells actually progresses at a more or less constant rate through all phases of T1D and that early-stage immunointervention will be both beneficial and desirable.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".