The Standards of Reporting of Neurological Disorders (STROND) checklist: Application to multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Descriptive epidemiological studies documenting the incidence and prevalence of multiple sclerosis (MS) and studies that report morbidity, mortality, and economic burden provide essential information for patients, healthcare providers, and policymakers. However, the quality of reporting of observational studies is often poor, limiting the ability to evaluate the validity of the findings. The Standards of Reporting of Neurological Disorders (STROND) reporting guideline comprises recommendations and a 15-item checklist of reporting items to aid high-quality reporting of incidence and prevalence studies of neurological disorders. METHODS: We explain the basic reporting items of the STROND checklist for the methods, results, and discussion sections in the context of the MS literature and searched for examples of good reporting of those items. RESULTS: We identified examples of good reporting of the basic reporting items from previous systematic reviews of the descriptive epidemiologic literature in MS. CONCLUSION: The adoption of the STROND reporting guidelines should improve the quality of reporting of descriptive epidemiological studies in MS. Along with efforts to improve methodological aspects of epidemiological studies and harmonization of data collection efforts, improved reporting could contribute to furthering our understanding of the epidemiology of 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 | 0.606 | 0.792 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.015 |
| Bibliometrics | 0.024 | 0.023 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.009 | 0.010 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".