Methods of Adverse Event Detection in Intensive Care: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: The objective of this systematic review was to characterize adverse event detection methods in the ICU setting, including neonatal, pediatric, and adult ICUs, to summarize the evidence of their performance characteristics. DATA SOURCES: Ovid MEDLINE, Ovid Embase, CINAHL, the Cochrane Library, and Google Scholar. STUDY SELECTION: Title and abstract screening, as well as full text review, were performed by two reviewers independently using Covidence software. Articles were included if they consisted of original research in a peer-reviewed journal with implementation of an adverse event detection method and reported the total number or category of adverse events, level of harm, or implementation of quality improvement (QI). DATA EXTRACTION: Data were extracted by two reviewers with 20% in duplicate. Extracted data included the study type and period/date, the adverse event detection method, the setting (location and type of ICU), ICU bed base, and the data of interest outlined above in study selection. DATA SYNTHESIS: Fifty-nine studies in neonatal, pediatric, and adult ICUs were included. Every category of adverse event detection was represented, including incident reporting (IR) (38 studies), trigger tool use (14 studies), trained observation (TO; 11 studies), and structured review (10 studies). TO identified the most adverse events per 100 patient days (57.3), and IR the least (6.4). Only 12 studies (20%) described QI initiatives. CONCLUSIONS: Detection methods likely need to be used in combination for comprehensive results. Definitions of adverse events and associated harms need to be standardized to facilitate future comparison and better understanding of the performance of individual methods of detection. In addition, more emphasis needs to be placed on the dissemination of practice change in response to detection. These will be important steps to better characterize high rates of adverse events in the ICU, thereby fueling patient safety initiatives.
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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 | 0.077 | 0.225 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".