Prevalence of Alexithymia in Patients with Medically Unexplained Physical Symptoms: A Cross-sectional Study in Egypt
Bibliographic record
Abstract
BACKGROUND: There is a high incidence of alexithymia in people who report medically unexplained symptoms. There have been limited studies on the prevalence of alexithymia in patients with medically unexplained physical symptoms (MUPS) in various ethnic and cultural backgrounds. OBJECTIVE: This study aimed to estimate the prevalence of alexithymia in patients with MUPS and examine their socio-demographic data. METHODS: In this cross-sectional study, 196 patients with MUPS were recruited from tertiary care internal medicine and neuropsychiatry clinics during the first quarter of 2019. Patients completed a structured interview; socio-demographic and medical history data were collected. Somatic symptom severity was assessed using the Arabic version of the Patient Health Questionnaire (PHQ-15). Alexithymia was assessed using the Arabic version of the Toronto Alexithymia Scale. RESULTS: General fatigue was the most common complaint observed, followed by headache and dyspepsia. In addition, 73.5% of patients had a high Patient Health Questionnaire score, 17.9% had somatic symptoms of medium severity, while 8% and 0.5% had low and marginal somatic symptoms, respectively. Alexithymia was presented in 49.5%, 22.9% had no alexithymia, and 27.6% had borderline/intermediate alexithymia.A weak positive correlation (r<0.4) was found between somatic symptom severity and alexithymic psychopathology (r=0.277;p<0.05). Only the 'difficulty identifying feelings' dimension of alexithymic psychopathology was positively correlated with the severity of somatic symptoms (r=0.271;p<0.05). CONCLUSION: Alexithymia is associated with the development of MUPS.
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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) | 0.002 | 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".