A Call for Improved Reporting of Human Islet Characteristics in Research Articles
Bibliographic record
Abstract
The increased availability of isolated human islets for diabetes research and the implementation of islet distribution programs in several countries worldwide have been driving forces behind the rapidly expanding number of scientific reports on studies using human islets. In Diabetologia , Hart and Powers (1) not only review the progress made in human islet research, made possible through their greater availability, but also identify the challenges associated with their use. These include the wide functional heterogeneity observed between islet preparations and the highly variable (and often inadequate) reporting of human islet characteristics in the scientific literature. Many factors contribute to the functional heterogeneity observed between human islet preparations. These include differences in donor characteristics (age, sex, ethnic background, health status prior to death, and cause of death); differences in pancreas procurement (isolation center, islet handling, estimated purity, and viability); warm and cold ischemia times; and tissue culture (1). All of these factors influence, to varying degrees, the morphology and function of the islets as investigated in the laboratory (2–5). For example, out of the 13 islet preparations from donors without diabetes for which islet cell composition is provided in the Human Pancreas Analysis Program PancDB database of the Human Islet Research Network …
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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 | 0.633 | 0.810 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.054 | 0.045 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.040 | 0.056 |
| Open science | 0.016 | 0.021 |
| Research integrity | 0.017 | 0.027 |
| Insufficient payload (model declined to judge) | 0.018 | 0.022 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".