Bibliographic record
Abstract
If we were to create the diabetes care experience anew, there is little doubt that it would not resemble the current bricks-and-mortar way we do things currently. For however a future model of care is designed, it would assume a digital-first approach, whereby the modern conveniences of digitally-mediated services we have experienced in other industries would be reflected in our diabetes care. To this end, our diabetes data would be liberated, transparent to those that need it, but safe and secure otherwise. We would have access to new tools that create insights that lower the burden, not add to it. And access to care would be just in time, convenient, and from a distance when needed. What is stopping a digital-first model is complex and deeply seated, but not insurmountable with engagement from industry, regulators, and care providers that are all willing to modernize the way care is delivered. Personal human interaction will continue to play an important part in the care for millions of people living with diabetes, no matter the sophistication of these digital services. What these technologies will provide is the human capacity to deal with the higher need, vulnerable people for whom access to timely care is an issue. Moreover, it will provide choice for an increasingly diverse population that seeks options for the form, and the delivery, of their personalized care.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".