PP230 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: FOLLOW UP NEEDS AFTER PAEDIATRIC CRITICAL CARE: A SURVEY STUDY
Bibliographic record
Abstract
Aims & Objectives: Despite a drive to establish post-PICU follow-up programs, there remains a gap in our understanding of current follow-up needs and healthcare use after PICU discharge. We aimed to understand: PICU survivors’ existing healthcare usage; primary care and specialist follow-up; return to school and work for patients and their caregivers; and whether there exists a perceived need for follow-up among PICU survivors. Methods: We administered a cross-sectional survey of patients surviving their PICU admission. Descriptive statistics were used to describe patient and caregiver characteristics and responses and Likert scale responses were summarized Results: 62 (45%) responses. Prior to PICU admission, 27% were known to a specialist. 61% were not taking regular medications and 23% had a prior PICU admission. Nearly one-third were admitted to PICU with an ALTRI, 61% were intubated and 45% had LOS>7days. Within three months after discharge, 39% had a new medication, 19% had revisited ED. 44% had follow-up within one week. 39% had new follow-up with a hospital specialist, 58% had follow-up with a new allied health professional. Of patients who attended school/daycare, 34% had not returned within three months of discharge and 23% of those children returning to school required a specialized education plan. Of working caregivers, 38% had missed more than one month of employment. Conclusions: This study demonstrates an ongoing reliance on healthcare services following discharge from the PICU. The potential financial burden associated with delays in caregivers return to work and the number of children requiring specialized education plans highlight the importance of supporting this vulnerable population Keywords: follow, outpatient, critical, PICS
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.078 | 0.015 |
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".