Assessment of alexithymia and cognition in elderly patients with depression
Bibliographic record
Abstract
Objectives: Depression is the most common psychiatric illness in the elderly. Alexithymia and cognitive impairment can be independently associated with depression and old age. This study aims to assess the alexithymia and cognitive dysfunction in geriatric patients with depression. Materials and Methods: A cross-sectional study was conducted on 100 participants of >60 years with depression. Participants were assessed using semi-structured pro forma, Geriatric Depression Scale (GDS), Hamilton Depression Rating Scale (HDRS), Toronto Alexithymia Scale-20 (TAS-20) having 3 subscales – “difficulty describing feeling” (DDF), “difficulty identifying feeling” (DIF), and “externally oriented thinking,” and Montreal Cognitive Assessment (MoCA). Statistical analysis was done using Chi-square/Fisher's exact test, Pearson's correlation, and t -test. Results: The mean age of the participants was 67.35 years with equal gender distribution. Thirty-four percent were >70 years of age and 53% from rural area. The median duration of depression was 30 months with a median duration of untreated illness, 6 months. Anxiety was the most common psychiatric comorbidity (43%). Seventy-one percent had alexithymia whereas 77% had cognitive impairment (MoCA score <26). Scores on GDS, HDRS, TAS-20, DIF, DDF, and MoCA (<26) were significantly higher in elder participants ( P < 0.05) and those from rural area ( P < 0.05). Higher TAS-20 score correlated with lower MoCA score ( P < 0.01). Furthermore, severe depression correlated with higher TAS-20 and lower MoCA score. Conclusion: More than two-third of participants had alexithymia and cognitive dysfunction. Higher alexithymia was associated with poor cognition. Severe depression correlated with higher alexithymia and cognitive impairment. Alexithymia and cognitive dysfunction were higher in the elderly from rural region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".