Haemoglobin glycosilated control and\npsychological features in patients with type 2\ndiabetes and cardiovascular problems: a pilot\nstudy
Bibliographic record
Abstract
Background: Psychological features are frequently implicated \nin haemoglobin glycosilated control. The purpose of the study \nis to compare haemoglobin glycosilated level and some psychological \nfeatures in outpatients with type 2 diabetes and cardiovascular \nproblems and low or high anxiety inclination. \nMaterials and methods: The median calculated for the ‘Trait \nanxiety’ scale of the State-Trait Anxiety Inventory permits to \ncreate a group with low anxiety inclination (Group 1) and a \ngroup with high anxiety inclination (Group 2). Nine outpaoutpatients \nwith low anxiety inclination (mean age ± SD \n= 58Æ44 ± 9Æ180; 66Æ7% male, 33Æ3% female) and seven \noutpatients with high anxiety inclination (mean age ± SD \n= 57Æ43 ± 11Æ830; 14Æ3% male, 85Æ7% female) articipated. We \nexamined group differences in haemoglobin glycosilated level, \ndepression, attributional styles (LCB) and alexithymia. Data \nwere analyzed using the Student’s t-test. \nResults: Compared to Group 1, Group 2 showed a more \nevident haemoglobin glycosilated level (P = 0Æ045), more \ndepression (P = 0Æ049) and showed more difficulties in identifying \nfeelings as a component of alexithymia (P = 0Æ050), \nwhereas there were no statistically significant differences in \nattributional style (LCB Internal, P = 0Æ611; LCB External, \nP = 0Æ890), alexithymia (P = 0Æ492) and in the other component \nof alexithymia (difficulties in describing feelings, P = 0Æ853; \nexternal thinking, P = 0Æ836). \nConclusions: Psychological features could be implicated in \nhaemoglobin control. Results are important for clinical staff as \nthey indicate where it is relevant to intervene in order to help \ndiabetic patients to have more haemoglobin glycosilated control.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".