Fatigue in Multiple Sclerosis Is Associated With Childhood Adversities
Bibliographic record
Abstract
Fatigue is a common and disabling symptom in patients with Multiple Sclerosis (PwMS). Its pathogenesis, however, is still not fully understood. Potential psychological roots, in particular, have received little attention to date. The present study examined the association of childhood adversities, specific trait characteristics, and MS disease characteristics with fatigue symptoms utilizing path analysis. Five hundred and seventy-one PwMS participated in an online survey. Standardized psychometric tools were applied. The Childhood Trauma Questionnaire (CTQ) served to assess childhood adversities. Trait variables were alexithymia (Toronto Alexithymia Scale; TAS-26) and early maladaptive schemas (Young Schema Questionnaire; YSQ). Current pathology comprised depression (Beck's Depression Inventory FastScreen; BDI-FS) and anxiety symptoms (State-Trait Anxiety Inventory; STAI-state), as well as physical disability (Patient determined Disease Steps; PDDS). The Fatigue Scale for Motor and Cognitive Functions (FSMC) was the primary outcome variable measuring fatigue. PwMS displayed high levels of fatigue and depression (mean FSMC score: 72; mean BDI-II score: 18). The final path model revealed that CTQ emotional neglect and emotional abuse remained as the only significant childhood adversity variables associated with fatigue. There were differential associations for the trait variables and current pathology: TAS-26, the YSQ domain impaired autonomy and performance, as well as all current pathology measures had direct effects on fatigue symptoms, accounting for 28.2% of the FSMC variance. Bayesian estimation also revealed indirect effects from the two CTQ subscales on FSMC. The final model fitted the data well, also after a cross-validation check and after replacing the FSMC with the Chalder Fatigue Questionnaire (CFQ). This study suggests an association psychological factors on fatigue in Multiple Sclerosis. Childhood adversities, as well as specific trait characteristics, seem to be associated with current pathology and fatigue symptoms. The article discusses potential implications and limitations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".