Core socioDemographic data variables in ICU Trials (CoDe-IT): a protocol for generating core data variables using a Delphi consensus process
Bibliographic record
Abstract
INTRODUCTION: Sociodemographic variables influence health outcomes, either directly (ie, gender identity) or indirectly (eg, structural/systemic racism based on ethnoracial group). Identification of how sociodemographic variables can impact the health of critically ill adults is important to guide care and research design for this population. However, despite the growing recognition of the importance of collecting sociodemographic measures that influence health outcomes, insufficient and inconsistent data collection of sociodemographic variables persists in critical care studies. We aim to develop a set of core data variables (CoDaV) for social determinants of health specific to studies involving critically ill adults. METHODS AND ANALYSIS: We will conduct a scoping review to generate a list of possible sociodemographic measures to be used for round 1 of the modified Delphi processes. We will engage relevant knowledge users (previous intensive care unit patients and family members, critical care researchers, critical care clinicians and research co-ordinators) to participate in the modified Delphi consensus survey to identify the CoDaV. A final consensus meeting will be held with knowledge user representatives to discuss the final CoDaV, how each sociodemographic variable will be collected (eg, level of granularity) and how to disseminate the CoDaV for use in critical care studies. ETHICS AND DISSEMINATION: The University of Calgary conjoint health research ethics board has approved this study protocol (REB22-1648).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.318 | 0.277 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.049 | 0.013 |
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".