Patient Flow in Congested Intensive Care Unit /Step-down Unit system: Premature Step-down or not?
Bibliographic record
Abstract
Abstract A Step-Down Unit (SDU) provides an intermediate Level of Care for patients from an Intensive Care Unit (ICU) as their condition becomes less acute. SDU congestion, as well as upstream patient arrivals, forces ICU administrators to incur costs, either in the form of overstays or premature step-downs. Basing on a proxy for patient acuity level called the ‘Nine Equivalents of Nursing Manpower Score (NEMS)’, patients were classified into two groups: high-acuity and low-acuity. Two patient flow policies were developed that select actions to optimize the system’s net health service benefit: one allowing for premature step-down actions, and the other allowing for patient rejection actions when the system is congested. The results show that the policy with patient rejection has a net health service benefit that significantly exceeds that of the policy with the premature step-down option. Based on these results, it can be concluded that premature step-down contributes to congestion downstream. Counter-intuitively, premature step-down should therefore be discouraged and patient rejection actions should be further explored as viable options for congested ICUs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.006 |
| 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; both teacher heads agree on what is shown here.
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".