32 How do Children with Medical Complexity Die? A Scoping Review
Bibliographic record
Abstract
Abstract Background While children with medical complexity (CMC) are recognized as an emerging and unique cohort, end of life remains poorly understood and little is known about illness trajectory, decision making and communication experiences for this group of patients and their families. Objectives This scoping review aimed to describe existing literature on the characteristics of end of life in CMC. Design/Methods The study was conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Electronic databases (MEDLINE, CINAHL, PsycINFO, Scopus, Embase) were searched up to June 2021. Studies reporting children, adolescents and young adults who were at end of life and fulfilled the definitional framework of medical complexity by Cohen et al were included by two independent reviewers. Data on study aims, design, methods, outcome measures and key findings were extracted, followed by quantitative and qualitative analysis of the results. Results Of 1535 publications initially identified, 23 studies were included. In terms of study characteristics, 20 (87.0%) studies were quantitative, 15 (65.2%) were published from 2015 to 2021 and 14 (70.0%) originated from the USA. Study outcomes were categorized into 5 main groups: (1) Place of death (30.8%), (2) Health care use (23.1%), (3) Interventions received or withdrawn (17.9%) (4) Decision making and communication (12.8%) and (5) Others (15.4%). These outcomes were found to be associated with sociodemographic factors and CMC diagnostic categories. Majority of CMC deaths occurred in hospitals and the mean proportion of hospital deaths reported was 68.8% (33.5% to 91.9%). Studies evaluated health care utilization at end of life in various settings including hospice, home care, hospital and the intensive care unit. Interventions studied in this cohort included mechanical ventilation, cardiopulmonary resuscitation, hemodialysis, procedures and medication use. Studies reported that CMC were subjected to more intensive interventions when compared to non-CMC. The 2 main themes which emerged from qualitative studies were that of advance care planning experiences and the unique end of life experiences of CMC and their families. Conclusion This scoping review highlighted the unique characteristics of end of life in CMC and outlined the emerging body of literature as well as knowledge gaps on this topic. A better understanding of this cohort of CMC would serve to inform clinical practice, service development and future research opportunities.
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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 | 0.014 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".