Development of a medical chart extraction tool to identify children with medical complexity in the Maritime Provinces of Canada
Bibliographic record
Abstract
Objectives: Children with medical complexity (CMC) are a population in need of policy and practice reform within the Canadian healthcare system. They are generally characterized as sharing four predominant characteristics: (1) one or more complex chronic condition(s), (2) functional limitations, (3) high health resources use, and (4) family-identified needs. There is currently no standard method to identify the CMC population in Canada. The aim of this research was to establish a clear method to select the best way to identify this population. This was done by developing a medical chart extraction tool specific to the Maritimes' population of CMC. Methods: This study was conducted in the Canadian Maritimes. The work was conducted in two phases; first, a consensus meeting was held to develop a Maritime-specific conceptual definition with a multidisciplinary group of experts. Second, a smaller expert team used the Maritime-specific definition to co-design a medical chart extraction tool. Ethical approval for this project was granted by IWK Health. Results: The consensus meeting involved a total of 57 relevant stakeholders from all three Maritime provinces. The definition developed through consensus included four constructs (functional limitations, chronic disease, health care use, and family-identified needs) and 12 descriptors (2 to 5 per construct). The medical chart extraction tool queried 22 items and 84 sub-items. Conclusions: The consensus process developed a strong and comprehensive medical chart extraction tool that can be applied to select the best-fit method for identifying CMC at a population level.
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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 | 0.022 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".