Development of the Standards of Reporting of Neurological Disorders (STROND) checklist
Bibliographic record
Abstract
BACKGROUND: Incidence and prevalence studies of neurologic disorders play an important role in assessing the burden of disease and planning services. However, the assessment of disease estimates is hindered by problems in reporting for such studies. Despite a growth in published reports, existing guidelines relate to analytical rather than descriptive epidemiologic studies. There are also no user-friendly tools (e.g., checklists) available for authors, editors, and peer reviewers to facilitate best practice in reporting of descriptive epidemiologic studies for most neurologic disorders. OBJECTIVE: The Standards of Reporting of Neurological Disorders (STROND) is a guideline that consists of recommendations and a checklist to facilitate better reporting of published incidence and prevalence studies of neurologic disorders. METHODS: A review of previously developed guidance was used to produce a list of items required for incidence and prevalence studies in neurology. A 3-round Delphi technique was used 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 neuroepidemiologic experts on the appropriateness of the items included in the checklist. FINDINGS: Of 38 candidate items, 15 items and accompanying recommendations were developed along with a user-friendly checklist. CONCLUSIONS: The introduction and use of the STROND checklist should lead to more consistent, transparent, and contextualized reporting of descriptive neuroepidemiologic studies resulting in more applicable and comparable findings and ultimately support better health care decisions.
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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 | 0.403 | 0.514 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.021 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".