Resource Utilization among Individuals Dying of Pediatric Life-Threatening Diseases
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Very little information exists on the number of resources utilized by individuals living with and dying of pediatric life-threatening diseases (LTDs). This study quantifies end of life (EOL) resource utilization among the pediatric population in British Columbia, Canada. METHODS: Data from Vital Statistics British Columbia were obtained for the pediatric population that died between 2002/03 and 2006/07. Our sample included three age groups: less than 1 year (excluding sudden infant death syndrome cases), 1 to 19 years, and 20 to 24 years. Using data from the Medical Services Plan and Discharge Abstract Database, we calculated annual rates of resources utilized (number of discharges, number of days in hospital, and number of medical services used) for every pediatric death that was due to an LTD in our selected 5-year time frame. Place of death was also explored. RESULTS: During the fiscal year of death and the fiscal year prior to death, children/adolescents and young adults dying of a pediatric LTD respectively experienced 5.3 and 3.7 hospital discharges, spent 48 and 39 days in the hospital, and required approximately 222 and 230 medical services. Infants were discharged once on average, and required 21 medical services. CONCLUSIONS: Resource utilization was very high for all three age groups, demonstrating the intense need for care by children dying of disease. These findings call for the strengthening of palliative care services in the province.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".