Validating the diagnosis of multiple sclerosis using Swedish administrative data in Värmland County
Bibliographic record
Abstract
OBJECTIVES: Multiple sclerosis (MS) is a chronic neurodegenerative disease of the central nervous system. Identifying MS at the population level is important for disease surveillance and allocation of resources. The Swedish National Patient Registry (NPR) has been used to study the epidemiology of MS, but the accuracy of this resource is not known. We aimed to validate a definition of MS using the Swedish NPR in Värmland County using a longitudinal cohort design. MATERIALS AND METHODS: Data were extracted from the NPR, the Total Population Register, the Swedish MS Register, and medical records for the years 2001-2013. Fifteen algorithms of hospitalizations and clinic visits for MS were developed and compared with findings in medical records, which acted as the "gold standard" definition. Sensitivity, specificity, and positive and negative predictive values (PPV, NPV) were estimated. RESULTS: Of 805 eligible persons identified in the NPR, 763 had MS (94.8%) according to medical records. Of these, 544 (71.3%) were also registered in the SMSreg. The case definition that had a well-balanced sensitivity and specificity required three or more clinic or hospital visits for MS (sensitivity of 85.3% (95% CI: 82.6-87.8) and specificity of 81.0% (95%CI: 65.9-91.4). CONCLUSIONS: Multiple case definitions with high sensitivity and moderate specificity were found, suggesting that the NPR can be used to accurately identify persons with MS.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".