Interventions to circumvent intensive care access block: a retrospective 2‐year study across metropolitan Melbourne
Bibliographic record
Abstract
OBJECTIVES: To measure the prevalence of interventions used to circumvent intensive care access block and to estimate the attributable mortality and additional hospital bed-days associated with them. DESIGN AND SETTING: Retrospective observational study of 11 adult public hospital intensive care units (ICUs) in Melbourne, Victoria, July 2004 - June 2006. MAIN OUTCOME MEASURES: Prevalence of five interventions in response to access block; attributable fatalities and/or increased length of stay associated with each. RESULTS: 21 896 ICU admissions and 3039 inhospital deaths (13.9%) were screened. All hospitals reported ICU access block. There were 6787 interventions for access block (mean, 9.3/day) -- 4070 (18.6% of admissions) instances of after-hours step-down from an ICU to a low-acuity ward; 1115 (5.1%) delays in an emergency department > 8 hours; 895 (4.1%) postponed major surgeries; 487 (2.2%) interhospital transfers; and 220 (1.0%) instances of premature cessation of intensive care. Based on published risk estimates, these interventions may have resulted in 91.1 (95% CI, 34.7-147.2) attributable deaths and 4368 (95% CI, 333-10 050) additional hospital bed-days each year. CONCLUSIONS: Intensive care access block is frequent, and measures to circumvent it increase mortality and length of stay. Further study of the health and financial implications of access block are warranted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".