Assessing the associations between selected screening variables from the Healthy Babies Healthy Children (HBHC) screening tool in relation to mortality, hospitalizations and emergency room visits among infants and children
Bibliographic record
Abstract
Background. Identifying infant and child risk, monitoring outcomes, and implementing supportive community programs are significant public health priorities. Screening for risk during the early postpartum period is optimal, and Canadian public health programs include a variety of screening tools and processes. Aim. This research aimed to inform the Ontario Healthy Babies Healthy Children (HBHC) screening process for identifying infants and children who may be at potential risk of mortality, hospital admissions, and emergency room visits. Methods. We conducted a retrospective longitudinal cohort study that included all women and their infants, meeting study criteria, who were born in 2013 in Ontario (N = 128,875). Using administrative data housed in ICES, 18 variables representing responses from the HBHC Screening Tool were evaluated to determine associations with the study outcomes. Associations with the outcomes were measured for infants from birth to 1 year and children from 1- 6 years. Sensitivity and specificity testing were completed to determine optimal cutoff points for identifying risk mortality and hospital admissions. Results. We demonstrated that the risk of infant mortality was associated with a cutoff point of 4 and 18 variables representing the HBHC screening responses. We further demonstrated that a reduced 9-variable model was equally as sensitive to establishing the risk of infant mortality using a cutoff point of 2. Other tested infant and child models were not as robust or sensitive, suggesting that additional variables influence the tested outcomes. Contribution. This study contributes to an existing body of knowledge that can inform the current HBHC screening protocol and process. Early postpartum screening is an initial step in risk identification. Further validation of tools and processes is warranted.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".