Infants, children, youth and young adults with a serious illness in British Columbia: a population-based analysis using linked administrative data
Bibliographic record
Abstract
BACKGROUND: Pediatric palliative care aims to improve quality of life among infants, children, youth and young adults with serious illnesses, sometimes over years, but estimates of infants, children, youth and young adults requiring pediatric palliative care have been highly variable and need refinement. We sought to describe this population in British Columbia and identify clinical instability to inform program planning in pediatric palliative care. METHODS: We conducted a population-based analysis using linked administrative health data from 2012/13 to 2016/17. We applied a coding framework validated in the United Kingdom to estimate the number of BC residents aged 0-25 years with serious illnesses and to identify 5 clinical stages. We describe demographics, estimate prevalence and model risk of instability, defined as having urgent hospital admissions, admissions to the intensive care unit or death. RESULTS: About 2500 infants, children, youth and young adults were admitted to hospital with a serious illness diagnosis each study year, of which around 50% were infants, 60% or so of whom had perinatal or congenital diagnoses. Compared with children aged 1-4 years, infants had the highest risk of instability (odds ratio [OR] 6.59, 95% confidence interval [CI] 5.97-7.29). Compared with oncology patients, infants, children, youth and young adults with neurological (OR 1.43, 95% CI 1.21-1.70) and otherwise specified diagnoses (OR 1.55, 95% CI 1.39-1.73) had a higher risk of instability. INTERPRETATION: The population of infants, children, youth and young adults with serious illnesses in BC is substantially larger than that currently receiving pediatric palliative care. Future planning of these services needs to consider expanding its reach, focusing particularly on infants and other subpopulations with high risk of instability.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".