A 6-Year Thematic Review of Reported Incidents Associated With Cardiopulmonary Resuscitation Calls in a United Kingdom Hospital
Bibliographic record
Abstract
BACKGROUND: Critical incident reporting can be applied to cardiopulmonary resuscitation (CPR) events as a means of reducing further occurrences. We hypothesized that local CPR-related events might follow patterns only seen after a long period of analysis. DESIGN: We reviewed 6 years of local incidents associated with cardiac arrest calls. The following search terms were used to identify actual or potential resuscitation events: "resuscitation," "cardio-pulmonary," "CPR," "arrest," "heart attack," "DNR," "DNAR," "DNACPR," "Crash," "2222." All identified incidents were independently reviewed and categorized, looking for identifiable patterns. SETTING: Nottingham University Hospitals is a large UK tertiary referral teaching hospital. RESULTS: A total of 1017 reports were identified, relating to 1069 categorizable incidents. During the same time, there were approximately 1350 cardiac arrest calls, although it should be noted that many arrest-related incidents were not associated with cardiac arrest call (e.g., failure to have the correct equipment available in the event of a cardiac arrest). Incidents could be broadly classified into 10 thematic areas: no identifiable incident (n = 189; 18%), failure to rescue (n = 133; 12%), staffing concerns (n = 134; 13%), equipment/drug concerns (n = 133; 12%), communication issues (n = 122; 10%), do-not-attempt-CPR decisions (n = 101; 9%), appropriateness of patient location or transfer (n = 96; 9%), concerns that the arrest may have been iatrogenic (n = 76; 7%), patient or staff injury (n = 43; 4%), and miscellaneous (n = 52; 5%). Specific patterns of events were seen within each category. CONCLUSIONS: By reviewing incidents, we were able to identify patterns only noticeable over a long time frame, which may be amenable to intervention. Our findings may be generalizable to other centers or encourage others to undertake this exercise themselves.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".