Intensity of end-of-life care among children with life-threatening conditions: a national population-based observational study
Bibliographic record
Abstract
Abstract Background Children with life-threatening conditions frequently experience high intensity care at the end of life, though most of this research only focused on children with cancer. Some research suggests inequities in care provided based on age, disease type, socioeconomic status, and distance that the child lives from a tertiary hospital. We examined: 1) the prevalence of indicators of high intensity end-of-life care (e.g., hospital stays, intensive care unit [ICU] stays, death in ICU, use of cardiopulmonary resuscitation [CPR], use of mechanical ventilation) and 2) the association between demographic and diagnostic factors and each indicator for children with any life-threatening condition in Canada. Methods We conducted a population-based retrospective cohort study using linked health administrative data to examine care provided in the last 14, 30, and 90 days of life to children who died between 3 months and 19 years of age from January 1, 2008 to December 31, 2014 from any underlying life-threatening medical condition. Logistic regression was used to model the association between demographic and diagnostic variables and each indicator of high intensity end-of-life care except number of hospital days where negative binomial regression was used. Results Across 2435 child decedents, the most common diagnoses included neurology (51.1%), oncology (38.0%), and congenital illness (35.9%), with 50.9% of children having diagnoses in three or more categories. In the last 30 days of life, 42.5% (n = 1035) of the children had an ICU stay and 36.1% (n = 880) died in ICU. Children with cancer had lower odds of an ICU stay (OR = 0.47; 95% CI = 0.36–0.62) and ICU death (OR = 0.37; 95%CI = 0.28–0.50) than children with any other diagnoses. Children with 3 or more diagnoses (vs. 1 diagnosis) had higher odds of > 1 hospital stay in the last 30 days of life (OR = 2.08; 95%CI = 1.29–3.35). Living > 400 km (vs < 50 km) from a tertiary pediatric hospital was associated with higher odds of multiple hospitalizations (OR = 2.09; 95%CI = 1.33–3.33). Conclusion High intensity end of life care is prevalent in children who die from life threatening conditions, particularly those with a non-cancer diagnosis. Further research is needed to understand and identify opportunities to enhance care across disease groups.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".