Identifying children with medical complexity in administrative datasets in a Canadian context: study protocol
Bibliographic record
Abstract
INTRODUCTION: Children with medical complexity and their families are an important population of interest within the Canadian healthcare system. Despite representing less than 1% of the paediatric population, children with medical complexity require extensive care and account for one third of paediatric healthcare expenditures. Opportunities to conduct research to assess disparities in care and appropriate allocation of health resources relies on the ability to accurately identify this heterogeneous group of children. This study aims to better understand the population of children with medical complexity in the Canadian Maritimes, including Nova Scotia (NS), New Brunswick (NB) and Prince Edward Island (PEI). This will be achieved through three objectives: (1) Evaluate the performance of three algorithms to identify children with medical complexity in the Canadian Maritimes in administrative data; then using the 'best fit' algorithm (2) Estimate the prevalence of children with medical complexity in the Canadian Maritimes from 2003 to 2017 and (3) Describe patterns of healthcare utilisation for this cohort of children across the Canadian Maritimes. METHODS AND ANALYSIS: The research will be conducted in three phases. In Phase 1, an expert panel will codevelop a gold-standard definition of paediatric medical complexity relevant to the Canadian Maritime population. A two-gate validation process will then be conducted using NS data and the gold-standard definition to determine the 'best fit' algorithm. During phase 2 the 'best fit' algorithm will be applied to estimate the prevalence of children with medical complexity in NS, NB and PEI. Finally, in phase 3 will describe patterns of healthcare utilisation across the Canadian Maritimes. ETHICS AND DISSEMINATION: Ethics approval for this protocol was granted by the institutional research ethics board at the IWK Health Centre (REB # 1026245). A waiver of consent was approved. This study will use an integrated knowledge translation approach, where end users are involved in each stage of the project, which could increase uptake of the research into policy and practice. The findings of this research study will be submitted for publication and dissemination through conference presentations and with our end users.
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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 | 0.051 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".