The German version of the Mini Suffering State Examination (MSSE) for people with advanced dementia living in nursing homes
Bibliographic record
Abstract
BACKGROUND: The Mini Suffering State Examination (MSSE) has been explicitly recommended to assess suffering in dementia patients. This study aimed to develop a German version of the MSSE and assess its psychometric properties involving people with advanced dementia (PAD) in a nursing home setting. METHODS: The MSSE was translated into German, and 95 primary nurses administered it cross-sectionally to 124 PAD in Zurich, Switzerland. The psychometric properties of the German MSSE version were calculated for this population. RESULTS: The mean age of the PAD was 83.3 years (SD = 9.1, range = 55-102 years), and 98 of them (79.0%) were women. The Kuder-Richardson Formula 20 coefficient for the entire scale (0.58), the eight items relating to objective health conditions (0.39), and the professional and family estimation of the patient's suffering (0.64) indicated low internal consistency. A confirmatory factor analysis indicated an unsatisfactory fit to a one-factor structure, with a comparative fit index and root mean square error of approximation of 0.71 and 0.08, respectively, and a Tucker-Lewis index of 0.64. The MSSE total score was significantly but moderately correlated with the total scores of the Symptom Management-End-of-Life with Dementia (SM-EOLD) scale (Pearson's correlation coefficient (r) = -0.44; p < 0.05), the physical suffering scores (r = 0.41; p < 0.05), and the psychological suffering scores (r = 0.55; p < 0.05). CONCLUSIONS: The German version of the MSSE questionnaire did not perform well in the nursing home setting involving PAD. The instrument had low internal consistency, doubtful validity, and could not discriminate between suffering and other distressing symptoms. We do not recommend its use in this population.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".