Advances in Exercise, Physical Activity, and Diabetes Mellitus
Bibliographic record
Abstract
IntroductionI n 2015/2016, a number of papers were published on the challenges of exercise management for patients living with diabetes.A big focus was on testing how the artificial pancreas might function during exercise, with or without activity announcements and the addition of glucagon.Moreover, several papers revealed barriers to exercise participation for people living with type 1 or type 2 diabetes.A few papers demonstrated that many of the tools for the preservation of glucose control during and after exercise in type 1 diabetes are not being used.This year, we selected 10 papers to highlight the field of exercise and diabetes, with an emphasis on ''technology'' rather than on the possible mechanisms for exercise action.Our initial search was restricted to human studies and primarily on studies in which patients with diabetes severed as subject participants.We screened over 150 papers on the topic that were found on Pubmed and other common search engines published between July 1, 2015 and June 30, 2016.The following 10 papers, we think, represent some of the highlights.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".