Agreement between administrative databases and medical charts for pregnancy‐related variables among asthmatic women
Bibliographic record
Abstract
PURPOSE: To determine the validity of pregnancy variables recorded in administrative databases of Quebec using patient medical charts as the gold standard among asthmatic pregnant women. METHODS: Three administrative databases were linked and provided information on maternal, pregnancy and infant characteristics for 726 pregnant asthmatic women who delivered in 1990-2000. Algorithms were developed to measure variables that were not recorded directly in the databases or to minimize the number of missing values for variables recorded in two or more databases. Medical file data were collected by two trained research nurses in 43 hospitals. The validity of categorical variables was assessed with sensitivity, specificity, predictive positive values (PPVs) and predictive negative values (PNVs), whereas the validity of continuous variables was assessed with Pearson correlation using the medical chart as the gold standard. RESULTS: The sensitivity of the sex of the baby, previous live birth and previous pregnancy ranged from 0.97 to 0.99. Corresponding figures were 0.92-0.98 for specificity. We also found high correlation coefficients, ranging from 0.875 to 0.999 for the length of gestation, dates of last menstruation and delivery, maternal age and birth weight. CONCLUSION: Pregnancy-related variables recorded in administrative databases or derived from algorithms based on two or more databases were found to be highly valid as compared to the medical chart among asthmatic women.
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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 | 0.019 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".