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 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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.032 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".