Mental comorbidity and multiple sclerosis: validating administrative data to support population-based surveillance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: While mental comorbidity is considered common in multiple sclerosis (MS), its impact is poorly defined; methods are needed to support studies of mental comorbidity. We validated and applied administrative case definitions for any mental comorbidities in MS. METHODS: Using administrative health data we identified persons with MS and a matched general population cohort. Administrative case definitions for any mental comorbidity, any mood disorder, depression, anxiety, bipolar disorder and schizophrenia were developed and validated against medical records using a a kappa statistic (k). Using these definitions we estimated the prevalence of these comorbidities in the study populations. RESULTS: Compared to medical records, administrative definitions showed moderate agreement for any mental comorbidity, mood disorders and depression (all k ≥ 0.49), fair agreement for anxiety (k = 0.23) and bipolar disorder (k = 0.30), and near perfect agreement for schizophrenia (k = 1.0). The age-standardized prevalence of all mental comorbidities was higher in the MS than in the general populations: depression (31.7% vs. 20.5%), anxiety (35.6% vs. 29.6%), and bipolar disorder (5.83% vs. 3.45%), except for schizophrenia (0.93% vs. 0.93%). CONCLUSIONS: Administrative data are a valid means of surveillance of mental comorbidity in MS. The prevalence of mental comorbidities, except schizophrenia, is increased in MS compared to the general population.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it