In‐hospital mortality after serious adverse events on medical and surgical nursing units: a mixed methods study
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To investigate the circumstances of nursing care eight hours before serious adverse events (=SAE's) on medical and surgical nursing units with subsequent in-hospital mortality in order to identify the extent to which these SAE's were potentially preventable. BACKGROUND: The prevention of SAE 's in acute care is coming under increasing scrutiny, while the role nursing care plays in the prevention of acute critical deterioration of patients is unclear. METHODS: Retrospective review of patient records of 63 SAE's in a Belgian teaching hospital where death was the final outcome following a cardiac arrest team call or unplanned ICU admission from an acute care unit. Data from chart reviews were combined with data regarding working conditions on the nursing unit at the time of the events and experts' opinions regarding the preventability of the outcomes. Finally, a pilot survey of staff nurses about their experiences with deteriorating patients and knowledge of vital signs and call criteria was conducted independently of the chart abstractions and case reviews. RESULTS: Experts were almost five times more likely to designate a case as potentially preventable when a cardiac arrest team call was the terminal event and were 40% less likely to designate a case as potentially preventable when more observations were documented in patient records. Survey results revealed that nurses were often unaware that their patients were deteriorating before the crisis. Nurses also reported threshold levels for concern for abnormal vital signs that suggested they would call for assistance relatively late in clinical crises. CONCLUSION: Renewed attention to accurate recording, documentation and interpretation of vital signs in hospital nursing practice appears needed. RELEVANCE TO CLINICAL PRACTICE: Timely detection of deteriorating patients to assist staff to improve their outcomes appears to be jeopardised by a number of practices and factors and merits deeper study.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".