A Pragmatic Method for Identification of Long-Stay Patients in the PICU
Bibliographic record
Abstract
OBJECTIVES: To develop a pragmatic method of identifying long-stay patients (LSPs) in the PICU. METHODS: We surveyed 40 expert stakeholders in 14 Canadian PICUs between February 2015 and March 2015 to identify key factors to use for defining LSPs in the PICU. We then describe a pragmatic method using these factors to analyze 523 admissions to an academic, tertiary-care PICU from February 1, 2015, to January 31, 2016. RESULTS: The overall response rate was 70% (28 of 40). Of respondents, 75% (21of 28) stated that it was important to define LSPs and identified present and future resource consumption (18 of 21 [86%] and 16 of 21 [76%], respectively) as the key reasons for defining LSPs. Respondents valued a definition that was consistent and ranked a percentile cutoff as the preferred analytic method for defining LSPs. Of respondents, 86% (24 of 28) though the LSP definition should include factors other than length of stay. We developed a surrogate marker for LSPs using mechanical ventilation and presence of a central venous catheter in our sample population to compare to varying percentile cutoffs. We identified 108 patients at the 80th percentile as LSPs who used 67% of total bed days and had a median length of stay of 11.3 days. CONCLUSIONS: We present a pragmatic method for the retrospective identification of LSPs in the PICU that incorporates unit- and/or patient-specific characteristics. The next steps would be to validate this method using other patient and/or unit characteristics in different PICUs and over time.
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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 | 0.085 | 0.210 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".