Indicators of intensive care unit capacity strain: a systematic review
Bibliographic record
Abstract
BACKGROUND: Strained intensive care unit (ICU) capacity represents a fundamental supply-demand mismatch in ICU resources. Strain is likely to be influenced by a range of factors; however, there has been no systematic evaluation of the spectrum of measures that may indicate strain on ICU capacity. METHODS: We performed a systematic review to identify indicators of strained capacity. A comprehensive peer-reviewed search of MEDLINE, EMBASE, CINAHL, Cochrane Library, and Web of Science Core Collection was performed along with selected grey literature sources. We included studies published in English after 1990. We included studies that: (1) focused on ICU settings; (2) included description of a quality or performance measure; and (3) described strained capacity. Retrieved studies were screened, selected and extracted in duplicate. Quality was assessed using the Newcastle-Ottawa Quality Assessment Scale (NOS). Analysis was descriptive. RESULTS: Of 5297 studies identified in our search; 51 fulfilled eligibility. Most were cohort studies (n = 39; 76.5%), five (9.8%) were case-control, three (5.8%) were cross-sectional, two (3.9%) were modeling studies, one (2%) was a correlational study, and one (2%) was a quality improvement project. Most observational studies were high quality. Sixteen measures designed to indicate strain were identified 110 times, and classified as structure (n = 4, 25%), process (n = 7, 44%) and outcome (n = 5, 31%) indicators, respectively. The most commonly identified indicators of strain were ICU acuity (n = 21; 19.1% [process]), ICU readmission (n = 18; 16.4% [outcome]), after-hours discharge (n = 15; 13.6% [process]) and ICU census (n = 13; 11.8% [structure]). There was substantial heterogeneity in the operational definitions used to define strain indicators across studies. CONCLUSIONS: We identified and characterized 16 indicators of strained ICU capacity across the spectrum of healthcare quality domains. Future work should aim to evaluate their implementation into practice and assess their value for evaluating strategies to mitigate strain. SYSTEMATIC REVIEW REGISTRATION: This systematic review was registered at PROSPERO (March 27, 2015; CRD42015017931 ).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| 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".