Factor analysis of the Montgomery Aasberg depression rating scale in an elderly stroke population
Bibliographic record
Abstract
BACKGROUND: Depression is frequent in elderly stroke patients, and the pathophysiology may involve psychological as well as organic mechanisms. AIM: To explore construct validity of the Montgomery Aasberg Depression Rating Scale using factor analysis and investigate whether symptom clusters of depression after stroke are associated with patient characteristics. METHODS: A sample of 163 stroke patients was assessed by the Montgomery Aasberg Depression Rating Scale. Pre-stroke assessment was accomplished by means of the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE), the Barthel ADL Index and patient's medical history. Post-stroke assessment was performed with the Mini Mental State Examination (MMSE), the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), the Star Cancellation Test, the Barthel ADL Index, the modified Rankin Scale (mRS) and the National Institute of Health Stroke Scale (NIHSS). Information was collected from the patients' records. A principal components factor analysis followed by oblique rotation was performed. RESULTS: Among the patients, 56.4% scored between 7 and 19 on the Montgomery Aasberg Depression Rating Scale, and 13% had a score above 19. The factor analysis resulted in three factors, called anhedonia (lassitude, inability to feel, suicidal thoughts, loss of appetite), sadness (observed sadness, reported sadness, pessimism) and agitation (inner tension, lack of concentration, disturbed sleep). Anhedonia correlated with cognitive impairment, whereas sadness correlated with sensorimotor and cranial nerve deficits. Agitation had low internal reliability and did not correlate with any systematic patients characteristics. CONCLUSION: We found three distinct factors. The factor anhedonia is related to cognitive impairment, sadness to neurological impairment due to the stroke and agitation to somatic factors not directly related to the stroke.
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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 | 0.003 | 0.006 |
| 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 | 0.000 |
| 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".