Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes mellitus is known to be associated with environmental, behavioral, and lifestyle factors such as a sedentary lifestyle, overly rich nutrition, and obesity. However, the day-to-day human-environment interactions and real-life activities that cause an individual's blood glucose to fluctuate remain relatively unexplored, owing in part to data collection challenges. This article presents a novel data collection system that overcomes these challenges and allows exploration of the spatial correlates of blood glucose fluctuation. METHODS: An automated monitoring system was developed combining a Global Positioning System (GPS) receiver with a continuous blood glucose monitor. The GPS was used to elicit a second-by-second accounting of an individual's daily activities alongside blood glucose measurement every 5 min. A pilot study of 40 diabetes patients was conducted over a 72-h period. Geographic Information System software was used to generate blood glucose maps, incorporating methods to deal with scale issues, overlapping data, and to protect subject identity. RESULTS: Individual blood glucose variation maps revealed a variety of distinct patterns. Most subjects had at least two major anchor points in their life combined with a variety of other activity locations at varying distances from home, many associated with quite distinct low or high blood glucose values. Further statistical analysis revealed location and distance from home were significantly correlated with blood glucose variation-although the strength and direction of the effect was quite mixed. CONCLUSIONS: Results suggests that blood glucose and space/location are highly correlated and should be considered further as a lifestyle-related risk factor for diabetes patients. In the future, patients and caregivers may benefit from individualized visualization tools that help identify problematic locations that require special attention.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".