The Relationship Between Remoteness and Outcomes in Critically Ill Children
Bibliographic record
Abstract
OBJECTIVE: A significant number of children live in remote geographic areas without direct access to tertiary care PICU. Our objective was to explore the relationship between remoteness and outcomes of critically ill children in Canada. DESIGN: Retrospective cohort study of patients admitted to the PICU from February 1, 2015, to January 31, 2016. SETTING: Pediatric tertiary care PICU in Canada. PATIENTS: All children admitted to PICU during the study period. INTERVENTIONS: None MEASUREMENTS AND MAIN RESULTS:: Four hundred fifty-five unique PICU admissions were included. One hundred sixty-nine patients were transported from another center of whom 28 lived in remote areas. For transported patients, remoteness (hazard ratio, 2.76, p < 0.001; hazard ratio, 2.22, p = 0.006), admission Pediatric Risk of Mortality (hazard ratio, 1.11; p = 0.001; hazard ratio, 1.05, p = 0.016), and transport by a noncritical care trained team (hazard ratio, 0.61, p = 0.021; hazard ratio, 0.66, p = 0.045) were associated with increased PICU and hospital lengths of stay, respectively. PICU mortality increased with duration of transport (odds ratio, 1.46; 95% CI, 1.09-1.97; p = 0.012). The odds of a remote-area patient being refused admission during the winter were significantly higher (odds ratio, 8.2; 95% CI, 3.0-22.3; p < 0.001) than a patient not requiring transport. Admission Pediatric Risk of Mortality score (4, interquartile range, 1-8 vs 2, interquartile range, 0-5; p = 0.001) and mortality rate (7.1%, 12/169 vs 0%, 0/286; p < 0.001) were significantly higher for transported than for nontransported patients. CONCLUSIONS: Remoteness was associated with increased PICU and hospital length of stay, and duration of transport was associated with higher admission Pediatric Risk of Mortality (PRISM) scores and mortality rates. Patients requiring transport had a significantly higher PICU mortality rate than those presenting directly to a tertiary care center. Further studies are needed to explore potential policy and healthcare resource implications of these findings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".