CONSEQUENCES OF PSYCHOSOCIAL FACTORS ON THE TREATMENT EFFICIENCY OF DIABETES MELLITUS TYPE 1 AND TYPE 2
Bibliographic record
Abstract
Background and Aims:The most common problem in patients withdiabetesmellitus(DM)type1(DM1) andtype2(DM2) is lack of compliance. Therefore, glycemic control largely depends ontreatmentadherence.u2028u2028Thefactorsof improvement of DM patients'treatmentefficiencywas studiedMethods:Study population (n=60) consists of: comparable groups (CG) with DM1 (n=16), DM2 (n=33) and newly diagnosed DM. Study data consists of demographic data, including: Medication Compliance Scale (MCS), Holmes and Rahe Stress Scale (HRSS), Dysfunctional attitudes Scale (DAS), Toronto Alexithymia Scale (TAS-20), The Depression, Anxiety and Stress Scale (DASS-21) and Chaban Quality of Life Scale (CQLS). Glycemic control was assessed by glycosylated hemoglobin (HbA1c) results. The statistics analysis has been performed using descriptive statistics and Pearson's correlation with SPSS Statistics 22.0.Results:Statistically significant difference was found, according to MCS: in patients with high (HC), middle (MC) and low level of compliance (LC). HC had higher rates according to CQLS on the level (p=0,004) and low rates of the DAS, TAS-20, DASS-21 and HRSS results (p=0,0001). LC had a higher level of HbA1c (M=13 SD:1vs. MC M=9.83, SD:1.4 vs. HC M=9, SD:1.4). The average values according to MCS: DM1 (M=19), DM2 (M=18) present MC; by HbA1c: DM1 (M=10), DM2 (M=10) have identical indicators. Therefore, no statistically significant difference was found between CG.Conclusions:The level of glycosylated hemoglobin is a sensitive marker of adherence to thetreatmentof patients withdiabetes, regardless of itstype
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".