Abstract P-039: DEFINING LONG-STAY PATIENTS IN THE PEDIATRIC INTENSIVE CARE UNIT: A SURVEY OF MEDICAL DIRECTORS, NURSE MANAGERS AND HOSPITAL ADMINISTRATORS
Bibliographic record
Abstract
Aims & Objectives: Long-stay patients (LSPs) account for ~8% of admissions to pediatric intensive care units (PICUs) and use up to 50% of hospital resources, but there is no consensus definition for LSPs. Therefore, we surveyed key PICU stakeholders to assess their perspectives regarding the importance of defining LSPs, the components the definition should include, the preferred method for deriving the definition, and the definitions’ characteristics. Methods We identified a purposive sample of one medical director, nurse manager and hospital administrator in each of 14 tertiary care academic PICUs across Canada. An internet-based survey was sent to 40 of 42 eligible participants in February and March of 2017. Results The participant flow diagram is shown in Figure 1. We had a response rate of 70% (28/40). 75% (21/28) of respondents stated it was important to define LSPs. Respondents thought such a definition was important to determine: PICU resource needs (86%, 18/21), alternative models of care (86%, 18/21), and current resource utilization (76%, 16/21) (Table 1). Respondents valued a definition that was consistent, and incorporated a percentile cut-off. 86% of respondents (24/28) felt the definition of LSPs should include factors other than PICU length of stay (Table 2).Conclusions PICU stakeholders believed defining LSPs is important. They indicated that this definition should be consistent, include a percentile cut-off for length of stay, and incorporate patient and unit-based factors along with PICU length of stay. Our results provide a basis for developing a consensus definition for LSPs in the PICU.
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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 | 0.009 | 0.018 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".