2008 Niday Perinatal Database quality audit: report of a quality assurance project
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: This quality assurance project was designed to determine the reliability, completeness and comprehensiveness of the data entered into Niday Perinatal Database. METHODS: Quality of the data was measured by comparing data re-abstracted from the patient record to the original data entered into the Niday Perinatal Database. A representative sample of hospitals in Ontario was selected and a random sample of 100 linked mother and newborn charts were audited for each site. A subset of 33 variables (representing 96 data fields) from the Niday dataset was chosen for re-abstraction. RESULTS: Of the data fields for which Cohen's kappa statistic or intraclass correlation coefficient (ICC) was calculated, 44% showed substantial or almost perfect agreement (beyond chance). However, about 17% showed less than 95% agreement and a kappa or ICC value of less than 60% indicating only slight, fair or moderate agreement (beyond chance). DISCUSSION: Recommendations to improve the quality of these data fields are presented.
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.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it