Finnish Registry-Based Protocol for Screening and Management of Fatigue and Cognitive Problems in Multiple Sclerosis: Observational Study
Bibliographic record
Abstract
Background: Digital patient registries are actively used to monitor long-term diseases. However, their potential in symptom management remains underused. Objective: This study aimed to report on the Finnish registry-based protocol to screen and manage cognitive symptoms and fatigue in multiple sclerosis (MS). Data on a sample collected during the first 2 years are presented. Methods: At the beginning of 2021, a Finnish protocol to screen and manage patient-perceived concerns related to cognition and fatigue, together with self-assessment of disease severity, symptoms, and quality of life (QoL) annually, was introduced. The Symbol Digit Modalities Test (SDMT), the Multiple Sclerosis Neuropsychological Questionnaire (MSNQ), the Fatigue Scale for Motor and Cognitive Functions (FSMC), as well as the Patient-Reported Expanded Disability Status Scale, the Visual Analog Scales, and the Euro QoL-5 Dimension were implemented into the Finnish MS registry. To support symptom management, patients were offered feedback reports based on the results of the FSMC and the MSNQ. The implementation of the protocol was evaluated in 5 Finnish wellbeing services counties. Results: Our sample from the beginning of 2021 to the end of 2022 includes data on 430 patients. A total of 86 (20%) patients have been assessed with the SDMT, whereas 329 (76.5%) patients have filled out the FSMC, and 172 (40.0%) patients have completed the MSNQ. The mean SDMT score is 49.0 (SD 13.56), MSNQ score is 35.3 (SD 9.39), total FSMC score is 63.0 (SD 22.49), and subscores for motor and cognitive fatigue are 31.6 (SD 11.43) and 31.5 (SD 11.68), respectively. The SDMT did not correlate with the MSNQ or the FSMC. Instead, the SDMT, MSNQ, and the FSMC correlated significantly with QoL. Conclusions: Fatigue and cognitive problems have an effect on QoL. In our preliminary sample, patient reports of cognitive problems and especially fatigue were conducted more frequently than the objective evaluation of processing speed. Although the Finnish MS registry offers a digital platform for the systematic screening of fatigue and cognitive problems, further education is needed to support the implementation of the protocol.
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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 | 0.026 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".