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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".