PP494 Topic: AS21–Post-PICU: Patient and Family Outcomes/Chronic Critical Illness/Post Intensive Care Syndrome in Pediatrics (PICS-p)/Post-discharge Care Delivery Models/Other: POST-PEDIATRIC INTENSIVE CARE HEALTHCARE UTILIZATION: A RETROSPECTIVE COHORT STUDY
Bibliographic record
Abstract
Aims & Objectives: Post-intensive care syndrome pediatrics (PICS-p) has been described in many Pediatric Intensive Care Unit (PICU) survivors. PICU follow-up clinics are gaining favour, however, little is known about current healthcare utilization of PICU survivors. We aimed to describe healthcare utilization following hospital discharge in children admitted to PICUs in Alberta, Canada. Methods: Retrospective cohort study of data extracted from linked provincial data sets for residents of Alberta, admitted to one of 3 PICUs from 2014-2017. Healthcare utilization is described in proportions and median (interquartile range(IQR)). Results: A total of 3755 PICU admissions were included. The median (IQR) age was 2 (0-9) years and 42.3% were female. Median (IQR) PICU length of stay (LOS) was 1.99 (1.05-7.12) days and hospital LOS was 6 (3-15) days. Within 5 years of discharge, 276 (7.31%) died, of which 174 (63%) died in hospital. Within the first year following discharge, 1470 (38.9%) were re-hospitalized with a median (IQR) of 1 (1-3) re-hospitalizations, and a median (IQR) of 9 (3-28) days in hospital. Participants had a median of 2 (1-4) Emergency Department (ED) visits; 4 (2-6) general practitioner visits; and 5 (2-9) pediatrician visits. Almost half of participants, 2492 (40.4%), visited all 3 physician categories at least once. Conclusions: Survivors of PICU admission have high rates of re-hospitalization, ED visits and outpatient physician use in the first year following PICU admission. This is consistent with described prevalence of PICS-p. Characterization of this healthcare use could inform targeted post-ICU care. Keywords: PICS-p, Surivvorship, PICU, Healthcare utilization
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".