Methods of detection of adverse events in critical care: a protocol for a systematic review
Bibliographic record
Abstract
INTRODUCTION: Adverse events, defined as unintended patient harm contributed to by healthcare, continue to increase morbidity, mortality and cost. Critically ill patients are at high risk of adverse events; however, the optimal approach to detection in this setting is unknown. Numerous approaches have been used, including voluntary reporting, chart reviews and trigger tools. The objective of this systematic review is to gain insight into the capacity of individual methods to detect adverse events in the intensive care unit (ICU), to inform implementation, and to facilitate quality improvement. METHODS AND ANALYSIS: Ovid MEDLINE, Ovid EMBASE, CINAHL, the Cochrane Library and Google Scholar were searched on 2 October 2023 for randomised controlled trials and observational studies evaluating the implementation or ongoing use of one or more systems of detection of adverse events in ICUs (neonatal to adult). Outcomes will include the total number of adverse events identified by detection method per 100 patient days (primary outcome), categories of adverse events, associated harm and whether detection informed quality improvement. A risk of bias assessment will be performed. The results will provide insight into each method's capacity to detect adverse events in addition to their associated severity. ETHICS AND DISSEMINATION: Ethics approval was not required as patient data will not be collected. A manuscript will be submitted to a peer-reviewed scientific journal. PROSPERO REGISTRATION NUMBER: CRD42024466584.
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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 | 0.119 | 0.136 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.016 | 0.017 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.056 | 0.010 |
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".