Understanding the economic, daily functioning, and diabetes management burden of non-severe nocturnal hypoglycemic events in Canada: differences between type 1 and type 2
Bibliographic record
Abstract
OBJECTIVE: To examine the daily functioning, diabetes management, and economic burden of non-severe nocturnal hypoglycemic events (NSNHEs) in Canada and differences in impacts by diabetes type. RESEARCH DESIGN AND METHODS: A 20-min web-based survey, with items derived from the literature, expert and patient interviews, assessing the impact of NSNHEs, was administered to patients with self-reported diabetes aged ≥18 having an NSNHE in the past month. RESULTS: Two thousand, two hundred and seventy-nine Canadian persons with diabetes were screened with 200 respondents meeting criteria and included in the analysis sample. Out of 87 working respondents, 15 reported on average 3.5 h of lost work (absenteeism). The reduction in work productivity (presenteeism) reported was comparable to the impact of arthritis. Other functional impacts included sleep and daily activities. Additionally, respondents' increased their usual blood sugar monitoring practice, on average, 4.2 (SD = 7.5) extra tests were conducted in the week following the event and reduced their insulin over the following 4.8 days. Increased healthcare utilization was also reported. Increased costs as a result of NSNHE for lost work productivity, increased diabetes management, and resource utilization was CAD 70.67 per person per year in this sample. Limitations of the study include the biases which are associated with a web-based survey and self-reported data. CONCLUSIONS: NSNHEs have serious consequences for patients and diabetes management practices. Greater attention to treatments which reduce NSNHEs can have a major impact on improving functioning while reducing the economic burden of diabetes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".