Comorbidity delays diagnosis and increases disability at diagnosis in MS
Bibliographic record
Abstract
BACKGROUND: Comorbidity is common in the general population and is associated with adverse health outcomes. In multiple sclerosis (MS), it is unknown whether preexisting comorbidity affects the delay between initial symptom onset and diagnosis ("diagnostic delay") or the severity of disability at MS diagnosis. OBJECTIVES: Using the North American Research Committee on Multiple Sclerosis Registry, we assessed the association between comorbidity and both the diagnostic delay and severity of disability at diagnosis. In 2006, we queried participants regarding physical and mental comorbidities, including date of diagnosis, smoking status, current height, and past and present weight. Using multivariate Cox regression, we compared the diagnostic delay between participants with and without comorbidity at diagnosis. We classified participants enrolled within 2 years of diagnosis (n = 2,375) as having mild, moderate, or severe disability using Patient Determined Disease Steps, and assessed the association of disability with comorbidity using polytomous logistic regression. RESULTS: The study included 8,983 participants. After multivariable adjustment for demographic and clinical characteristics, the diagnostic delay increased if obesity, smoking, or physical or mental comorbidities were present. Among participants enrolled within 2 years of diagnosis, the adjusted odds of moderate as compared to mild disability at diagnosis increased in participants with vascular comorbidity (odds ratio [OR] 1.51, 95% CI 1.12-2.05) or obesity (OR 1.38, 95% CI 1.02-1.87). The odds of severe as compared with mild disability increased with musculoskeletal (OR 1.81, 95% CI 1.25-2.63) or mental (OR 1.62, 95% CI 1.23-2.14) comorbidity. CONCLUSIONS: Both diagnostic delay and disability at diagnosis are influenced by comorbidity. The mechanisms underlying these associations deserve further investigation.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".