The Impact of Alexithymia on Treatment Response in Psychiatric Disorders: A Systematic Review
Bibliographic record
Abstract
Treatment of psychiatric disorders relies heavily on a trial and error approach, often prolonging the time required to obtain symptomatic improvements. The identification of reliable predictors of treatment response is instrumental to enact an individualized approach. Alexithymia represents a personality trait reflecting an intrinsic difficulty in recognizing the emotional components of subjective experiences. Thus, its modulating role on treatment outcome has gathered substantial attention during the past years. In the present paper, we aimed at exploring the available evidence for Alexithymia role in influencing the treatment outcome on a wide range of psychiatric conditions by means of a systematic review. DATA SOURCE: We performed a systematic review in Medline and Scopus, augmented by tracking the reference list of the pertinent articles. INCLUSION CRITERIA: To be included in this review, research studies had to assess alexithymia impact on a treatment intervention delivered to manage a primary psychiatric disorder. STUDY EVALUATION AND DATA SYNTHESIS: After removing duplicates, titles were screened first, then abstracts, and last full texts were read, eventually leading to the inclusion or exclusion of the papers according to the criteria established before the online search. Then results of the search were downloaded in.xml format and uploaded in Rayyan, a free web software, that helps expedite the initial screening of abstracts and titles using a process of semi-automation while incorporating a high level of usability. After uploading, screening of the literature was performed in blind by two investigators. Disagreement between reviewers was resolved by joint discussion with a third senior investigator. The quality of evidence was assessed using the Newcastle Ottawa Scale. Thereafter, the data considered relevant was extracted and synthetized in this paper. RESULTS: Our search yielded a total of 30 articles dealing with a wide range of psychiatric conditions and exploring both pharmacological and psychotherapeutic interventions. Several lines of evidence suggest a complex role for alexithymia in influencing the psychiatric treatment outcome, further underscoring the need for additional research in this area to better address the existing knowledge gaps.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".