How Do Children With Medical Complexity Die? A Scoping Review
Bibliographic record
Abstract
Introduction: Advancement in medical expertise and technology has led to a growing cohort of children with medical complexity (CMC), who make up a rising proportion of childhood deaths. However, end of life in CMC is poorly understood and little is known about illness trajectories, communication, and decision-making experiences. Objective: To synthesize existing literature and characterize the end-of-life experience in CMC. Methods: A literature search of MEDLINE, CINAHL, PsycINFO, Scopus, Embase, and Google Scholar was conducted up to August 26, 2021. Studies reporting CMC at end of life were included and the extracted data were analyzed descriptively. Findings: Of 1535 publications identified, 23 studies were included. Most studies (15/23 [65%]) were published from 2015 to 2021 and were quantitative in nature (20/23 [87%]). The majority of studies that extracted data from a single country (18/20 [90%]) originated from North America. Study outcomes were categorized into four main domains: (1) place of death (2) health care use (3) interventions received or withdrawn (4) communication, and end-of-life experiences. The weighted percentage of in-hospital CMC deaths was 80.6%. Studies reported that CMC had increased health care use and were subjected to more intensive interventions at end of life compared with non-CMC. Qualitative studies highlighted the following themes: Intrinsic prognostic uncertainty, differing perspectives of the child's quality of life, the chronic illness experience, a desire to have parental expertise acknowledged, surprise at the terminal event, the experience of multiple losses, with an overarching theme of the need for compassionate care at end of life. Conclusions: This scoping review highlighted important characteristics of end of life in CMC, outlining the emerging evidence and knowledge gaps on this topic. A better understanding of this cohort of seriously and chronically ill children would serve to inform clinical practice, service development, and future research.
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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 | 0.010 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".