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 …
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".