The effect on fall rate of blood glucose testing at the time of falls in elderly diabetics
Bibliographic record
Abstract
OBJECTIVE: To determine the pattern of blood sugar and HbA1c testing among supportive living residents with diabetes and whether, in those with diabetes, blood glucose measurement was done at the time of a fall. RESEARCH DESIGN AND METHODS: The management of diabetes in relation to falls in the supportive living sector is unknown. A cross-sectional questionnaire study in Edmonton Alberta, Canada of Designated Supportive Living (DSL) homes have places funded by Alberta Health Services and other homes (SL) that have no funded places. A questionnaire was distributed to Directors of Care/managers of supportive living homes, with telephone interview follow-up if required. RESULTS: Sixty responses from 61 of the 71 homes (86%) provided information. 21 were DSL and 39 were SL homes. DSL homes were significantly more likely than SL ones to report that residents with diabetes had blood glucose measurements as part of regular care, to be aware that glycosylated haemoglobin was measured, and to say that blood glucose was measured at the time of a fall. Regression analysis identified that facilities with a policy to measure blood glucose at the time of a fall had a lower rate of falls in residents with diabetes than facilities without such a policy (p < 0.05). No effect of this policy was seen in residents without diabetes. CONCLUSION: Residents with diabetes were less likely to fall in homes that indicated that they had a policy to measure blood glucose at the time of a fall.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".