A new record linkage for assessing infant mortality rates in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: Infant mortality statistics for Canada have routinely omitted Ontario-Canada's most populous province-as a high proportion of Vital Statistics infant death registrations could not be linked with their corresponding Vital Statistics live birth registrations. We assessed the feasibility of linking an alternative source of live birth information with infant death registrations. METHODS: All infant deaths occurring before 365 days of age registered in Ontario's Vital Statistics in 2010-2011 were linked with birth records in the Canadian Institute for Health Information's hospitalization database. Crude birthweight-specific and gestational age-specific infant mortality rates were calculated, and rates examined according to maternal and infant characteristics. RESULTS: Of 1311 infant death registrations, only 47 (3.6%) could not be linked to a hospital birth record. The overall crude infant mortality rate was 4.7 deaths per 1000 live births (95% CI, 4.4 to 4.9), the same as previously reported for the rest of Canada in 2011. Infant mortality was higher in women < 20 years (5.8 per 1000 live births) and ≥ 40 years (5.9 per 1000 live births), and lowest among those aged 25-29 years (3.9 per 1000 live births). Infant mortality was notably higher in the lowest (5.1 per 1000 live births) residential income quintile than the highest (3.4 per 1000 live births). CONCLUSION: Use of birth hospitalization records resulted in near-complete linkage of all Vital Statistics infant death registrations. This approach could enhance the conduct of representative surveillance and research on infant mortality when direct linkage of live birth and infant death registrations is not achievable.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".