Effect of a standard schema of self-monitoring blood glucose in patients with poorly controlled, non-insulin-treated type 2 diabetes mellitus: A controlled longitudinal study
Bibliographic record
Abstract
The effect of self-monitoring of blood glucose (SMBG) on glycemic control with regard to non-insulin-treated Type 2 diabetes mellitus (NIT-Type 2 DM) is still a controversial topic. Against this backdrop, we sought to compare the effect of a continuous short-term SMBG schema with as-usual treatment, based on changes in oral antidiabetic treatment in patients with poorly controlled Type 2 DM. We reviewed 492 NIT-Type 2 DM record charts, selecting 27 patients, with poor glycemic control, who were thought to self-monitor their blood glucose levels (SMBG group). We then compared them with 27 patients treated with modifying drugs or diets to achieve and maintain the glycemic target (Control Group). Haemoglobin A1c (HbA1c) and fasting plasma glucose (FPG) were evaluated at baseline, after 3 and 6 months. HbA1c values decreased after 3 and 6 months in the SMBG group (P < 0.001 on both occasions) and in the control group (P < 0.05 and P < 0.01, respectively), but without a significant difference between the two groups when compared at the same time. The FPG progressively decreased in both groups, reaching a significant difference in the SMBG group after 3 months and in the control group after 6 months, and without a significant difference between the two groups. The SMBG schema used in our study could be adopted for target groups before proceeding to the next therapeutic enhancement drug step, representing a useful tool that can help diabetic patients in raising awareness of and treating their disease.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".