Descriptions of health states associated with increasing severity and frequency of hypoglycemia: a patient-level perspective
Bibliographic record
Abstract
AIMS: We sought to develop descriptions of health states associated with daytime and nocturnal hypoglycemia in a structured fashion from the patient's perspective under different combinations of severity and frequency of hypoglycemic events. METHODS: An expert meeting followed by two patient focus groups was used to develop comprehensive descriptions of acute consequences of severe and non-severe, daytime and nocturnal hypoglycemia. Patients with diabetes (type 1 = 85, type 2 = 162) from a survey panel then validated these descriptions and assessed how often they worried and took different actions to prevent hypoglycemia. Severity and frequency of hypoglycemia were compared with respect to how often people worried and took actions to prevent an event. The effect of hypoglycemia on 35 different life activities was quantitatively compared for patients who had and had not experienced a severe hypoglycemic event. RESULTS: At least 95% of respondents agreed that the detailed patient-level descriptions of health states accurately reflected their experience of severe and non-severe, daytime and nocturnal hypoglycemia, thereby validating these descriptions. Respondents who had experienced a severe hypoglycemic event were generally more adversely affected in their worries and actions and life events than those who experienced only non-severe events; those who experienced nocturnal events were more affected than those who experienced only daytime events. CONCLUSION: The negative psychosocial consequences and undesirable compensatory behaviors arising from hypoglycemia underscore the importance of preventing severe episodes, particularly severe nocturnal episodes. These validated descriptions for hypoglycemia from the patient's perspective may also help inform future qualitative and quantitative research.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".