Validation of a Self-Report Comorbidity Questionnaire for Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND/AIMS: Researchers increasingly recognize the high frequency of comorbidity in multiple sclerosis (MS) and the negative impact on quality of life and disability, but little work has evaluated methods of comorbidity measurement in MS. We aimed to validate a self-report questionnaire for assessing comorbidity in MS. METHODS: Patients with MS were recruited from the MS Clinic in Winnipeg, Canada and the Mellen Center (Cleveland Clinic, Cleveland, Ohio, USA) from October 2008 to 2009. Using a questionnaire, participants reported the presence or absence of 36 comorbidities, sociodemographic characteristics, and disability status. Abstractors blinded to questionnaire results collected data regarding the comorbidities of interest and their treatments. Using the medical record as the gold standard, we determined the sensitivity, specificity, positive and negative predictive values of the questionnaire data. To measure agreement we calculated kappa (kappa) statistics. RESULTS: We enrolled 404 participants. Agreement between self-report and medical records was high (kappa >0.82) for diabetes and hypertension; substantial (kappa = 0.62-0.80) for hyperlipidemia, thyroid disease, glaucoma, and lung disease; moderate (kappa = 0.43-0.56) for osteoporosis, irritable bowel syndrome, migraine, depression, heart disease, and anxiety disorders. Agreement was slight to fair for the remaining comorbidities. CONCLUSIONS: Self-report is a valid way to capture comorbidities affecting MS patients.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".