Establishing indicators to monitor the utilization of POC glucose meters in glycemic control
Bibliographic record
Abstract
OBJECTIVES: The use of point-of-care (POC) glucose meters and effectiveness of glycemic control should be monitored as part of quality assurance practices. This study is the first to establish indicators to evaluate the utilization of glucose meters in our academic hospital. METHODS: Patient results from glucose meters located in emergency department (ED), intensive care units (ICUs), general wards, and neonatal units were extracted from the data management system for the months of October to December from 2021 to 2023. Six indicators were developed and compared across clinical units, including: glucose test number per meter, daily frequency of patient glucose testing 1-4 times (%), ratio of POC to core lab glucose testing, and percentages of patient glucose results within target ranges, below critical level 2.5 mmol/L, or above 25.0 mmol/L. RESULTS: The six indicators varied greatly between clinical units due to the differences in patient populations, clinical scenarios, and clinical guidelines. About 90 % of patients in general wards, ED and neonatal units were tested glucose 1-4 times/day, while 27.1 % patients in ICUs were tested glucose 5-10 times/day or more. The average POC/core lab glucose testing ratio in neonatal units, general wards, ICUs, and ED was 17.6, 14.9, 2.2, and 0.3, respectively. Overall, 69.6 % of patient glucose results in all clinical units fell within the target ranges. Percentages of patient glucose results below 2.5 mmol/L or above 25.0 mmol/L were both under 0.6 % across clinical units. CONCLUSIONS: In this study, the indicators were able to assess the use of POC glucose meters and the effectiveness of glycemic control and to identify opportunities for quality improvement. The approach can be readily applied in other hospitals.
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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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".