The Impact of the Organization of High-Dependency Care on Acute Hospital Mortality and Patient Flow for Critically Ill Patients
Bibliographic record
Abstract
RATIONALE: Little is known about the utility of provision of high-dependency care (HDC) that is in a geographically separate location from a primary intensive care unit (ICU). OBJECTIVES: To determine whether the availability of HDC in a geographically separate unit affects patient flow or mortality for critically ill patients. METHODS: Admissions to ICUs in the United Kingdom, from 2009 to 2011, who received Level 3 intensive care in the first 24 hours after admission and subsequently Level 2 HDC. We compared differences in patient flow and outcomes for patients treated in hospitals providing some HDC in a geographically separate unit (dual HDC) with patients treated in hospitals providing all HDC in the same unit as intensive care (integrated HDC) using multilevel mixed effects models. MEASUREMENTS AND MAIN RESULTS: In 192 adult general ICUs, 21.4% provided dual HDC. Acute hospital mortality was no different for patients cared for in ICUs with dual HDC versus those with integrated HDC (adjusted odds ratio, 0.94 [0.86-1.03]; P = 0.16). Dual HDC was associated with a decreased likelihood of a delayed discharge from the primary unit. However, total duration of critical care and the likelihood of discharge from the primary unit at night were increased with dual HDC. CONCLUSIONS: Availability of HDC in a geographically separate unit does not impact acute hospital mortality. The potential benefit of decreasing delays in discharge should be weighed against the increased total duration of critical care and greater likelihood of a transfer out of the primary unit at night.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".