ALEXITHYMIA AND ASSOCIATED FACTORS IN THE ELDER PATIETNS SUFFERING FROM DIABETES MELLITUS
Bibliographic record
Abstract
Objective: To determine the alexithymia traits and associated risk factors in the elder patients suffering from DM (Diabetes Mellitus). Methodology: This research work was a descriptive study carried out in Nishter Hospital, Multan on 120 patients suffering from Diabetes Mellitus. We performed the collection of data with the utilization of personal data form and the TAS (Toronto Alexithymia Scale). Results: Alexithymia prevailed in 75.80% elder patients who were suffering from Diabetes Mellitus. We discovered that these patients faced difficulties in the identification and description of their feelings. All of these patients were present with high externally-oriented styles of thinking. The level of patients, structure of family and duration of illness influenced their manifestation of traits of alexithymia. Conclusion: Most of the elder patients suffering from Diabetes Mellitus exhibited the characteristics of alexithymia. But these characteristics were not present to have association with gender, patient’s age, marital status, level of qualification and occupational status. Additionally, there was no association between alexithymia and HbA1c (Glycosylated Hemoglobin), BMI (Body Mass Index), PBG (Post-prandial Glucose) and the complications and treatment associated with Diabetes Mellitus. Keywords: Glycosylated Hemoglobin, Diabetes Mellitus, Oriented, Glucose, Post-Prandial Glucose, Body Mass Index.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".