Construction and management of ARDS/sepsis registry with REDCap.
Bibliographic record
Abstract
OBJECTIVE: The study aimed to construct and manage an acute respiratory distress syndrome (ARDS)/sepsis registry that can be used for data warehousing and clinical research. METHODS: The workflow methodology and software solution of research electronic data capture (REDCap) was used to construct the ARDS/sepsis registry. Clinical data from ARDS and sepsis patients registered to the intensive care unit (ICU) of our hospital formed the registry. These data were converted to the electronic case report form (eCRF) format used in REDCap by trained medical staff. Data validation, quality control, and database management were conducted to ensure data integrity. RESULTS: The clinical data of 67 patients registered to the ICU between June 2013 and December 2013 were analyzed. Of the 67 patients, 45 (67.2%) were classified as sepsis, 14 (20.9%) as ARDS, and eight (11.9%) as sepsis-associated ARDS. The patients' information, comprising demographic characteristics, medical history, clinical interventions, daily assessment, clinical outcome, and follow-up data, was properly managed and safely stored in the ARDS/sepsis registry. Data efficiency was guaranteed by performing data collection and data entry twice weekly and every two weeks, respectively. CONCLUSIONS: The ARDS/sepsis database that we constructed and manage with REDCap in the ICU can provide a solid foundation for translational research on the clinical data of interest, and a model for development of other medical registries in the future.
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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 | 0.027 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".