Bibliographic record
Abstract
Purpose: The goal of this study is a review of the literature on the role of alexithymia among stroke patients, especially assessing its impact on the process of convalescence, psychiatric comorbidity and clinical outcomes. Views: Organic alexithymia is a common post-stroke complication, which interferes with many aspects of health among stroke survivors, with interleukin-18 having an important role in the appearance and depth of alexithymia. Post-stroke patients suffering from alexithymia are more prone to depression, manifestations of which may differ from the symptoms of this shown by the non-alexithymic post-stroke population. Alexithymia is also the cause of more severe symptoms of stroke itself, as well as more severe symptoms of post-stroke post-traumatic stress disorder. It is often associated with right-hemisphere lesions, but there are findings which show that left brain insult may also be the cause; the explanation is that information transfer from right to left hemisphere causes disturbances in emotional awareness. Conclusions: Alexithymia plays an important role in the health of post-stroke patients. Assessment of the trait should be considered in their population, since it plays an important role in terms of predicting psychiatric comorbidity and severity of stroke complications, which transfers to treatment choice. Further research is required to examine other post-stroke psychiatric comorbidities associated with alexithymia and to closely determine the response of rehabilitation, including pharmacological treatment and psychological therapy.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".