Abstract T P408: Suboptimal Quality of Reporting of Neuroimaging Methods for Studies of Cerebral Small Vessel Disease
Bibliographic record
Abstract
Introduction: The quality of reporting of neuroimaging methods for studies of cerebral small vessel disease is unknown. We systematically reviewed studies of MRI white matter hyperintensities (WMH) of vascular origin to determine the frequency of reporting of key aspects of neuroimaging methods, and whether reporting varied by sample size, study design or journal impact factor. Methods: Three raters independently reviewed 100 consecutive papers reporting WMH severity, either as a primary outcome or covariate, to abstract 50 study characteristics based on the published STRIVE standards (Wardlaw et al Lancet Neurol 2013). Final determinations were made by consensus. An aggregate quality score (range 0-11) was created by adding one point for reporting of each of 11 key characteristics (Table). Spearman correlation or chi-square test, as appropriate, were used to test associations with quality score. Results: Papers were published between 2009 and 2013 with journal impact factors ranging from 0.56 to 15.3, with cohort (79%) and case control (21%) studies represented. Quantitative computational methods were used in 28 studies. MR field strength, MRI sequence types, type of WMH measurement method, blinding and number of raters were reported frequently, but reporting of other characteristics was inconsistent (Table). Study quality score was not correlated with journal impact factor, sample size or cohort study design. Conclusions: There is inconsistent reporting of neuroimaging methods in the small vessel disease imaging literature. Increased adherence to published reporting standards, such as the STRIVE criteria, may facilitate more objective peer review of submitted manuscripts and increase the reproducibility of published results. More work is needed to facilitate adoption of standards and checklists by authors, reviewers and editors.
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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 | 0.451 | 0.723 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".