Defining a low-risk birth cohort: a cohort study comparing two perinatal data sets in Ontario, Canada
Bibliographic record
Abstract
IntroductionThere are two main data sources for perinatal data in Ontario, Canada: the BORN BIS and CIHI-DAD. Such databases are used for perinatal health surveillance and research, and to guide health care related decisions. ObjectivesOur primary objective was to examine the level of agreement between the BIS and CIHI-DAD. Our secondary objectives were to identify the differences between the data sources when identifying a low-risk birth (LRB) cohort and to understand their implications. MethodsWe conducted a population-based cohort study comparing characteristics and clinical outcomes of all linkable births in BIS and CIHI-DAD between 1$^{\rm st}$ April 2012 and 31$^{\rm st}$ March 2018. We excluded out-of-hospital births, those with invalid healthcare numbers, non-Ontario residents and gestational age < 20 weeks. We compared the portion of the cohort that met the criteria of a provincial definition of LRB based on each data source and compared clinical outcomes between the groups. ResultsDuring the study period, 779,979 eligible births were linkable between the two data sources. After applying the LRB exclusions, there were 129,908 cases in the BIS and 136,184 cases in CIHI-DAD. Most exclusion criteria had almost perfect, substantial or moderate agreement. The agreement for non-cephalic presentation and BMI ≥ 40 kg/m2 (kappa coefficients 0.409 and 0.256, respectively) was fair. Comparison between the two LRB cohorts identified differences in the prevalence of cesarean (14.3% BIS versus 12.0% CIHI-DAD) and NICU admission (8.7% BIS versus 7.5% CIHI-DAD) and only 0.01% difference in the prevalence of ICU admission. ConclusionsOverall, we found high levels of agreement between the BIS and CIHI-DAD. Identifying a LRB cohort in either database may be appropriate, with the caveat of appropriate understanding of the collection, coding and definition of certain outcomes. The decision for selecting a database may depend on which variables are most important in a particular analysis.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".