Does Alexithymia Predict the Psychiatric Comorbidity Among Healthy Carriers of Hepatitis B?
Bibliographic record
Abstract
Objective: The psychiatric disorders among healthy carriers of hepatitis B (HBsAg), who have no severe physical disability or any medical treatment, have clinical importance. We aimed to research the comorbid psychiatric disorders and alexithymia and to identify whether alexithymia and accompanying somatic symptoms predict the presence of psy-chiatric diagnoses or not among HBsAg carriers. Methods: Eighty-nine healthy carriers of Hepatitis B patients and nınety-three healthy indi-viduals were included to study. Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition) (DSM-IV) (SCID-I), Hamilton Depression Rating Scale (HAM-D), Hamilton Anxiety Rating Scale (HAM-A), and Toronto Alexithymia Scale (TAS) were applied. Results: When the distribution of SCID I psychiatric diagnoses among healthy HBsAg car-riers examined, majority of the patients (n = 53, 59.6%) had any psychiatric diagnosis. The logistic regression model evaluating whether number of somatic symptoms and alexi-thymia predict the psychiatric diagnosis, we observed that number of somatic symptoms predicted the presence of psychiatric diagnosis (odds ratio = 2.762, P < .001). Conclusion: Our findings revealed that alexithymia may potentiate the occurrence of psy-chiatric disorders in such patients and that it requires more consideration. So, our results suggest that HBsAg carriers need multidisciplinary evaluation including hepatology, infection clinics and psychiatric liaison.
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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 | 0.000 | 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) | 0.003 | 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".