Prevalence and correlates of alexithymia in older persons with medically (un)explained physical symptoms
Bibliographic record
Abstract
OBJECTIVES: Much is unknown about the combination of Medically Unexplained Symptoms (MUS) and alexithymia in later life, but it may culminate in a high disease burden for older patients. In the present study we assess the prevalence of alexithymia in older patients with either MUS or Medically Explained Symptoms (MES) and we explore physical, psychological and social correlates of alexithymia. METHODS AND DESIGN: A case control study was performed. We recruited older persons (>60 years) with MUS (N = 118) or MES (N = 154) from the general public, general practitioner clinics and hospitals. Alexithymia was measured by the 20-item Toronto Alexithymia Scale, correlates were measured by various questionnaires. RESULTS: Prevalence and severity of alexithymia were higher among older persons with MUS compared to MES. Alexithymia prevalence in the MUS subgroup was 23.7%. We found no association between alexithymia and increasing age. Alexithymia was associated with depressive symptoms, especially in the MUS population. CONCLUSIONS: Alexithymia prevalence was lower than generally found in younger patients with somatoform disorder, but comparable to studies with similar diagnostic methods for MUS. Considering the high prevalence and presumed etiological impact of alexithymia in older patients with MUS, as well as its association with depression, this stresses the need to develop better understanding of the associations between alexithymia, MUS and depression in later life.
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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 | 0.002 |
| 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".