Explanation and Elaboration of the Standards of Reporting of Neurological Disorders Checklist: A Guideline for the Reporting of Incidence and Prevalence Studies in Neuroepidemiology
Bibliographic record
Abstract
BACKGROUND: Incidence and prevalence studies of neurological disorders play an extremely important role in hypothesis-generation, assessing the burden of disease and planning of health services. However, the assessment of disease estimates is hindered by the poor quality of reporting for such studies. We developed the Standards of Reporting of Neurological Disorders (STROND) guideline in order to improve the quality of reporting of neurological disorders from which prevalence, incidence, and outcomes can be extracted for greater generalisability. METHODS: The guideline was developed using a 3-round Delphi technique in order to identify the 'basic minimum items' important for reporting, as well as some additional 'ideal reporting items.' An e-consultation process was then used in order to gauge opinion by external neuroepidemiological experts on the appropriateness of the items included in the checklist. FINDINGS: The resultant 15 items checklist and accompanying recommendations were developed using a similar process and structured in a similar manner to the Strengthening of the Reporting of Observational Studies in Epidemiology checklist for ease of use. This paper presents the STROND checklist with an explanation and elaboration for each item, as well as examples of good reporting from the neuroepidemiological literature. CONCLUSIONS: The introduction and use of the STROND checklist should lead to more consistent, transparent and contextualised reporting of descriptive neuroepidemiological studies that should facilitate international comparisons, and lead to more accessible information for multiple stakeholders, ultimately supporting better healthcare decisions for neurological disorders.
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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 | 0.262 | 0.407 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".