Regional variation of pediatric interhospital critical care transport in Japan
Bibliographic record
Abstract
BACKGROUND: An in-depth understanding of the epidemiology of ill or injured children transported between hospitals is crucial in building regional medical transport services in public health. Although the epidemiological situation varies by nation and region, it has not been well documented in Japan. In this report we described the number of pediatric interhospital transportations and examined the regional variations and trends in the recent decade. METHODS: We performed repeated, cross-sectional analyses of children (<15 years) undergoing interhospital transportation in 2010, 2013, and 2016-19, using the national database of public ambulances of the Fire and Disaster Management Agency in Japan. We stratified the cases into critical care transport (CCT) or non-critical care transport (NonCCT) by the illness/injury severity of the transported children. We calculated the national population-adjusted number of CCTs and described prefectural variations in CCT numbers with analytical thinking. RESULTS: There were 23,506 CCTs and 138,347 NonCCTs. The national average of population-adjusted CCT numbers was 255 per 1,000,000 person-years. The statistics varied by prefectures, ranging from 25-536 per 1,000,000 person-years. The annual trends were also diverse across prefectures, increasing in nine, decreasing in six, and static in 31 prefectures. In analytical thinking of regional variations, potential contributing factors included available interhospital transport services and the threshold of direct admission and referral to tertiary-care hospitals, whereas regional variations were not well associated with geographical patterns or population size. CONCLUSIONS: Public ambulance services were substantially used for CCTs and NonCCTs in Japan. Regional variations should be taken into account for the future policymaking on pediatric interhospital transportation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".