Consensus Recommendations for Standardized Data Elements, Scales, and Time Segmentations in Studies of Human Circadian/Diurnal Biology and Stroke
Bibliographic record
Abstract
Increasing evidence indicates that circadian and diurnal rhythms robustly influence stroke onset, mechanism, progression, recovery, and response to therapy in human patients. Pioneering initial investigations yielded important insights but were often single-center series, used basic imaging approaches, and used conflicting definitions of key data elements, including what constitutes daytime versus nighttime. Contemporary methodologic advances in human neurovascular investigation have the potential to substantially increase understanding, including the use of large multicenter and national data registries, detailed clinical trial data sets, analysis guided by individual patient chronotype, and multimodal computed tomographic and magnetic resonance imaging. To fully harness the power of these approaches to enhance pathophysiologic knowledge, an important foundational step is to develop standardized definitions and coding guides for data collection, permitting rapid aggregation of data acquired in different studies, and ensuring a common framework for analysis. To meet this need, the Leducq Consortium International pour la Recherche Circadienne sur l'AVC (CIRCA) convened a Consensus Statement Working Group of leading international researchers in cerebrovascular and circadian/diurnal biology. Using an iterative, mixed-methods process, the working group developed 79 data standards, including 48 common data elements (23 new and 25 modified/unmodified from existing common data elements), 14 intervals for time-anchored analyses of different granularity, and 7 formal, validated scales. This portfolio of standardized data structures is now available to assist researchers in the design, implementation, aggregation, and interpretation of clinical, imaging, and population research related to the influence of human circadian/diurnal biology upon ischemic and hemorrhagic stroke.
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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 | 0.585 | 0.695 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.029 |
| Bibliometrics | 0.027 | 0.022 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.031 | 0.019 |
| Research integrity | 0.027 | 0.042 |
| Insufficient payload (model declined to judge) | 0.012 | 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".