Prevalence of multimorbidity and its impact on survival in people with motor neuron disease
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: This study was undertaken to determine the prevalence of multimorbidity in people with motor neuron disease (MND) and to identify whether specific patterns of multimorbidity impact survival beyond age alone. METHODS: We performed a retrospective analysis of the Scottish national MND register from 1 January 2015 to 29 October 2019. People with amyotrophic lateral sclerosis, primary lateral sclerosis, progressive muscular atrophy, or progressive bulbar palsy were included. We fitted latent class regression models incorporating comorbidities (class indicators), age, sex, and bulbar onset (covariates), and survival (distal outcome) with multimorbidity as a hypothesised latent variable. We also investigated the association between the Charlson Comorbidity Index and survival in Cox regression and compared its discrimination and calibration to age alone. RESULTS: A total of 937 people with MND were identified (median age = 67 years, 60.2% male); 64.8% (n = 515) had two or more comorbidities. We identified a subpopulation with high prevalence of cardiovascular disease, but when accounting for the relationship between age and individual comorbidities, there was no difference in survival. Both Charlson Comorbidity Index (hazard ratio [HR] per unit increase = 1.11, 95% confidence interval [CI] = 1.07-1.15, p < 0.0001) and age (HR per year increase = 1.04, 95% CI = 1.03-1.05, p < 0.0001) were significantly associated with survival, but discrimination was higher for age compared to Charlson Comorbidity Index (C-index = 0.63 vs. 0.59). CONCLUSIONS: Multimorbidity is common in MND, necessitating holistic interdisciplinary management, but age is the dominant predictor of prognosis in people with MND. Excluding people with MND and multimorbidity from trial participation may do little to homogenise the cohort in terms of survival potential and could harm generalisability.
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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 | 0.010 |
| 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 | 0.001 | 0.001 |
| 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".