Validation of Canadian mothers’ recall of events in labour and delivery with electronic health records
Bibliographic record
Abstract
BACKGROUND: Maternal report of events that occur during labour and delivery are used extensively in epidemiological research; however, the validity of these data are rarely confirmed. This study aimed to validate maternal self-report of events that occurred in labour and delivery with data found in electronic health records in a Canadian setting. METHODS: Data from the All Our Babies study, a prospective community-based cohort of women's experiences during pregnancy, were linked to electronic health records to assess the validity of maternal recall at four months post-partum of events that occurred during labour and delivery. Sensitivity, specificity and kappa scores were calculated. Results were stratified by maternal age, gravidity and educational attainment. RESULTS: Maternal recall at four months post-partum was excellent for infant characteristics (gender, birth weight, gestational age, multiple births) and variables related to labour and delivery (mode of delivery, epidural, labour induction) (sensitivity and specificity >85%). Women who had completed a university degree had significantly better recall of labour induction and use of an epidural. CONCLUSION: Maternal recall of infant characteristics and events that occurred during labour and delivery is excellent at four months post-partum and is a valid source of information for research purposes.
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".