Health-related quality of life associated with daytime and nocturnal hypoglycaemic events: a time trade-off survey in five countries
Bibliographic record
Abstract
BACKGROUND: Hypoglycaemic events, particularly nocturnal, affect health-related quality of life (HRQoL) via acute symptoms, altered behaviour and fear of future events. We examined the respective disutility associated with a single event of daytime, nocturnal, severe and non-severe hypoglycaemia. METHODS: Representative samples were taken from Canada, Germany, Sweden, the United States and the United Kingdom. Individuals completed an internet-based questionnaire designed to quantify the HRQoL associated with different diabetes- and/or hypoglycaemia-related health states. HRQoL was measured on a utility scale: 1 (perfect health) to 0 (death) using the time trade-off method. Three populations were studied: 8286 respondents from the general population; 551 people with type 1 diabetes; and 1603 with type 2 diabetes. Respondents traded life expectancy for improved health states and evaluated the health states of well-controlled diabetes and diabetes with non-severe/severe and daytime/nocturnal hypoglycaemic events. RESULTS: In the general population, non-severe nocturnal hypoglycaemic events were associated with a 0.007 disutility compared with 0.004 for non-severe daytime episodes, equivalent to a significant 63% increase in negative impact. Severe daytime and nocturnal events were associated with a 0.057 and a 0.062 disutility, respectively, which were not significantly different. CONCLUSIONS: This study applies an established health economic methodology to derive disutilities associated with hypoglycaemia stratified by onset time and severity using a large multinational population. It reveals substantial individual and cumulative detrimental effects of hypoglycaemic events - particularly nocturnal - on HRQoL, reinforcing the clinical imperative of avoiding hypoglycaemia.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".