Alexithymia as Predictor of Treatment Outcome in Patients with Functional Gastrointestinal Disorders
Bibliographic record
Abstract
OBJECTIVE: A previous study found a strong association between alexithymia and functional gastrointestinal disorders (FGID). The objective of this study was to investigate whether alexithymia might be a predictor of treatment outcome in patients with FGID. METHODS: A group of FGID outpatients classified by the 'Rome I' criteria was divided into improved (N= 68) and unimproved (N= 44) groups on the basis of pre-established criteria after 6 months of treatment. Patients were administered the 20-item Toronto Alexithymia Scale, the Hospital Anxiety and Depression Scale, and the Gastrointestinal Symptom Rating Scale both before and after 6 months of treatment. RESULTS: At the base-line assessment, compared with the improved patients, the unimproved patients had significantly higher levels of anxiety, depression, alexithymia, and gastrointestinal symptoms. Stability of alexithymia was demonstrated by significant correlations between base-line and follow-up TAS-20 scores in the entire sample. Moreover, hierarchical regression analyses showed that the stability of TAS-20 scores over the 6-month treatment period could not be accounted for by their associations with anxiety and depression scores. In logistic regression analyses, base-line alexithymia and depression emerged as significant predictors of treatment outcome. Relative to depression, however, alexithymia was the stronger predictor. CONCLUSIONS: Alexithymia is a reliable and stable predictor of treatment outcome in FGID patients. Although further studies are needed, clinicians might improve treatment outcome by identifying patients with high alexithymia, and attempting to improve these patients' skills for coping with emotionally stressful situations.
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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) | 0.001 | 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".