Qualitative subjective assessment of a high‐resolution database in a paediatric intensive care unit—Elaborating the perpetual patient's ID card
Bibliographic record
Abstract
OBJECTIVE: The main purpose of our study was to subjectively assess the quality of a paediatric intensive care unit (PICU) database according to the Directory of Clinical Databases (DoCDat) criteria. DESIGN AND SETTING: A survey was conducted between April 1 and June 15, 2018, among the Sainte Justine PICU research group. POPULATION: Every member of this group whose research activity required the use of the database and/or who was involved in the development/validation of the database. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: All 10 research team members (one Information Technology specialist, one junior medical student, and eight clinician researchers) who used the high-resolution database fulfilled the survey (100% response rate). The median quality level of the Sainte Justine PICU database across all the 10 criteria was 3 (2-4), rated on a 1 (worst) to 4 (best) numeric scale. When compared with previously assessed databases through the DoCDat criteria, we found that the Sainte Justine PICU database performance was similar. CONCLUSIONS: The PICU high-resolution database appeared of good quality when subjectively assessed by the DoCDat criteria. Further validation procedures are mandatory. We suggest that data quality assessment and validation procedures should be reported when creating a new database.
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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 | 0.070 | 0.163 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".