Facilitating access to glucometer reagents increases blood glucose self‐monitoring frequency and improves glycaemic control: a prospective study in insulin‐treated diabetic patients
Bibliographic record
Abstract
AIMS: To investigate whether availability of glucometer reagents increases the frequency of self-blood glucose monitoring (SBGM) and improves glycaemic control in diabetic patients. METHODS: Sixty-two insulin-treated diabetic patients were randomized to two groups, matched for age, gender, education, income, type and duration of diabetes, years of insulin treatment, number of daily insulin injections, and haemoglobin (Hb)A1c. All patients were given a glucometer, but one group (no cost, NC) was provided glucometer test strips free of charge. The other group (control, C) had to purchase strips as they found it necessary. Both groups of patients were followed longitudinally at 2-monthly intervals for 12 months with measurement of blood glucose and HbA1c, and the frequency of SBGM was determined by downloading the glucometer memory. RESULTS: The SBGM frequency was significantly higher in the NC group vs. the C group during the first 4 months (2.0 +/- 0.2 tests/day vs. 1.4 +/- 0.1 tests/day, P<0.025). Mean HbA1c remained stable over the 12 months in the NC group, whereas an increase with time was observed in the C group. The difference in HbA1c between the two groups was significant (P<0.002) after 6 months. Random blood glucose measured at each visit and average glucose recorded by the glucometer were also lower in the NC group vs. the C group (P<0.005). There was a negative correlation between HbA1c and SBGM frequency, and HbA1c in patients testing at least twice a day was lower than in those testing less than twice a day (8.8 +/- 0.2% vs. 9.6 +/- 0.2%, P<0.001). CONCLUSIONS: In this prospective study, having easy access to glucometer strips provided free of charge to patients increased SBGM frequency. The relationship between HbA1c and SBGM frequency supports the view that SBGM is an essential tool in diabetes management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".