Validation Process of a High-Resolution Database in a Pediatric Intensive Care Unit -- Describing the Perpetual Patient's Validation
Bibliographic record
Abstract
Objective: High data quality is essential to ensure the validity of clinical and research inferences based on it. However, these data quality assessments are often missing even though these data are used in daily practice and research. Our objective was to evaluate the data quality of our high-resolution electronic database (HRDB) implemented in our pediatric intensive care unit (PICU). Design: A prospective validation study of a HRDB. Setting: A 32-bed pediatric medical, surgical and cardiac PICU in a tertiary care freestanding maternal-child health center in Canada. Population: All patients admitted to the PICU with at least one vital sign monitored using a cardiorespiratory monitor connected to the central monitoring station. Interventions: None Measurements and Main Results: Between June 2017 and August 2018, data from 295 patient days were recorded from medical devices and 4,645 data points were video recorded and compared to the corresponding data collected in the HRDB. Statistical analysis showed an excellent overall correlation (R2=1), accuracy (100%), agreement (bias=0, limits of agreement=0), completeness (2% missing data) and reliability (ICC=1) between recorded and collected data within clinically significant pre-defined limits of agreement. Divergent points could all be explained. Conclusions: This prospective validation of a representative sample showed an excellent overall data quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".