Alexithymia and somatization in psychiatric and medical patients
Bibliographic record
Abstract
315 subjects comprising of equal number of psychiatric and medical patients; and control group were randomly selected for the study. Neuroses and depressive illness of the psychiatric group was chosen through psychiatric clinic. The medical outpatients attending physician clinic formed the medical group while the control group was identified from staff and patients relatives. The selected patient was administered a series of questionnaires such as Toronto Alexithymia Scale (TAS), Middlesex Hospital Questionnaires (MHQ) and Duke Health Profile (DUKE) after the diagnosis \nwas confirmed. The psychiatric patient was further evaluated using Hamilton Depression Rating Scale (HDS) and Hamilton Anxiety Rating Scale (HAS) for assessment of severity of depression and anxiety respectively.172 (55%) subjects had positive TAS score and consider as alexithymia. The prevalence of alexithymia was significantly higher in psychiatric and medical patients \nthan the control group. The number of alexithymia among patients with underlying depression and anxiety were significantly increased. In general individual with \nalexithymia had significantly higher number of personality trait than healthy volunteer. There were marked psychosocial impairments and physical disability in \npsychiatric and medical patients respectively. However the difference was small and did not reach the level of significant. Although we cannot link alexithymia with a \nspecific factor, most likely it is the product of personality disturbances aggravated by medical and psychiatric illness, especially depression and anxiety.
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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 | 0.001 | 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) | 0.003 | 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".