Intensive Care Unit Bed Availability and Outcomes for Hospitalized Patients With Sudden Clinical Deterioration
Bibliographic record
Abstract
BACKGROUND: Intensive care unit (ICU) beds, a scarce resource, may require prioritization of admissions when demand exceeds supply. We evaluated the effect of ICU bed availability on processes and outcomes of care for hospitalized patients with sudden clinical deterioration. METHODS: We identified consecutive hospitalized adults in Calgary, Alberta, Canada, with sudden clinical deterioration triggering medical emergency team activation between January 1, 2007, and December 31, 2009. We compared ICU admission rates (within 2 hours of medical emergency team activation), patient goals of care (resuscitative, medical, and comfort), and hospital mortality according to the number of ICU beds available (0, 1, 2, or >2), adjusting for patient, physician, and hospital characteristics (using data from clinical and administrative databases). RESULTS: The cohort consisted of 3494 patients. Reduced ICU bed availability was associated with a decreased likelihood of patient admission within 2 hours of medical emergency team activation (P = .03) and with an increased likelihood of change in patient goals of care (P < .01). Patients with sudden clinical deterioration when zero ICU beds were available were 33.0% (95% CI, -5.1% to 57.3%) less likely to be admitted to the ICU and 89.6% (95% CI, 24.9% to 188.0%) more likely to have their goals of care changed compared with when more than 2 ICU beds were available. Hospital mortality did not vary significantly by ICU bed availability (P = .82). CONCLUSION: Among hospitalized patients with sudden clinical deterioration, we noted a significant association between the number of ICU beds available and ICU admission and patient goals of care but not hospital mortality.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".