Follow-up needs after paediatric critical care: A survey
Bibliographic record
Abstract
OBJECTIVES: This survey aims to describe the perceived needs for follow-up, and the actual follow-up received, by caregivers of Paediatric Intensive Care Unit (PICU) survivors. It explores PICU survivors' existing healthcare usage, primary care and specialist follow-up, and return to school and work for patients and their caregivers, respectively. METHODS: A cross-sectional survey of patients surviving their PICU admission at a quaternary care children's hospital. Patients admitted less than 24 hours or who were not expected to survive were excluded. Descriptive statistics were used to describe characteristics and responses, and Likert scale responses were summarized. RESULTS: Of the 139 patients consented, 62 (45%) completed the survey. Among children who attended school/daycare, 34% had not returned within 3 months of PICU discharge and 23% of those children returning to school required a new specialized education plan. Among employed caregivers, 38% had missed more than 1 month of employment. After discharge, 39% of patients had follow-up scheduled with a hospital specialist and 53% had new allied health follow-up. Of the respondents, 59% agreed or strongly agreed that follow-up after PICU would be beneficial for their child, and 84% agreed or strongly agreed that they would attend an in-person PICU follow-up appointment. CONCLUSIONS: This survey demonstrates a perceived need for follow-up among some caregivers of PICU survivors, an ongoing reliance on healthcare services, and school absenteeism following PICU admission. Further work is required to better delineate the ideal timing and format of follow-up, as well as the population most likely to benefit.
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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 | 0.003 | 0.009 |
| 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 | 0.001 | 0.001 |
| 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".