Abstract 20047: Nasa Model of "Threat and Error" in Paediatric Cardiac Surgery: Death Typically Results From Cycles of Error That Originate in the Operating Room and Are Amplified by Additional Error in Intensive Care
Bibliographic record
Abstract
Introduction: We introduced the NASA “threat and error model” to our surgical unit; all admissions are considered “flights”, which should pass through stepwise de-escalations in risk. Hypothesis: Errors significantly influence risk de-escalation and contribute to poor outcomes. Methods: Patient flights (524) were tracked real-time for threats, errors and unintended states (figure). Expected risk de-escalation was: wean from mechanical support, sternal closure, extubation, ICU discharge and discharge home. Data were accrued via performance personnel, bedside data, reporting mechanisms and staff interviews. Infographics of flights were openly discussed weekly. Results: In 12% (64/524) of flights, the child failed to de-escalate sequentially through expected risk levels; unintended increments instead occurred. Failed de-escalations were highly associated with errors (426; 257 flights), however seemingly benign (P<.0001). Errors with clinical consequence (263; 173 flights) had 29% rate of failed de-escalations vs 4% (P<.0001). The most dangerous errors were “apical” errors typically (84%) occurring in the OR which led to cycles of propagating unintended states (n=110): these had 43% (47/110) rate of failed de-escalation (vs 4%, P<.0001). Apical errors were triggered by identifiable threats in 25% (28/110) (usually - 75% - morphology/ comorbidities); 75% were instead “unforced” errors. Cycles of unintended state were often (46%) amplified by additional (up to 7) errors in ICU that would worsen clinical deviation. Overall, failed de-escalations in risk were extremely closely linked to brain injury (N=13; P<.0001), or death (N=7; P<.0001). Conclusions: Deaths and brain injury almost always occur from propagating error cycles that originate in the OR and are often amplified by additional ICU errors. Improvements in threat management, error detection/rescue and vigilance at times of failed de-escalation will translate into improved outcomes.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".