Internal Audit of the Canadian Neonatal Network Data Collection System
Bibliographic record
Abstract
Background Neonatal databases worldwide have become a prominent tool for benchmarking, evaluation of outcomes, and quality improvement initiatives. We aimed to assess the precision of the Canadian Neonatal Network (CNN) database by conducting an internal audit of data extraction. Methods An audit was conducted in all 31 neonatal units participating in the CNN. Ninety-five data items selected for reabstraction were classified into categories (critical, important, less important) based on predefined agreement rates. Five records were randomly selected at each site for reabstraction, including one short (3–7 days), two medium (8–12 days), and two long (18–22 days) stay cases. Agreement rates for each data item were calculated for individual units and across the network. Results A total of 155 cases and 14,725 data fields were reabstracted. The overall agreement rates for critical, important, and less important data items were 98.0, 96.1, and 96.3%, respectively. Individual site variation for discrepancies ranged between 0.2 and 12.8% for all collected data items. Conclusion Neonatal data extraction within the CNN database structure exhibited high precision; thereby, revealing the reliability of our data abstraction for neonatal demographic, processes of care, and outcomes information. An independent external audit of data extraction would be beneficial.
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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 | 0.122 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".