FIASCO II failure to achieve a satisfactory cardiac outcome study: the elimination of system errors
Bibliographic record
Abstract
OBJECTIVES: Death in low-risk cardiac surgical patients provides a simple and accessible method by which modifiable causes of death can be identified. In the first FIASCO study published in 2009, local potentially modifiable causes of preventable death in low-risk patients with a logistic EuroSCORE of 0-2 undergoing cardiac surgery were inadequate myocardial protection and lack of clarity in the chain of responsibility. As a result, myocardial protection was improved, and a formalized system introduced to ensure clarity of the chain of responsibility in the care of all cardiac surgical patients. The purpose of the current study was to re-audit outcomes in low-risk patients to see if improvements have been achieved. METHODS: Patients with a logistic EuroSCORE of 0-2 who had cardiac surgery from January 2006 to August 2012 were included. Data were prospectively collected and retrospectively analysed. The case notes of patients who died in hospital were subject to internal and external review and classified according to preventability. RESULTS: Two thousand five hundred and forty-nine patients with a logistic EuroSCORE of 0-2 underwent cardiac surgery during the study period. Seven deaths occurred in truly low-risk patients, giving a mortality of 0.27%. Of the seven, three were considered preventable and four non-preventable. Mortality was marginally lower than in our previous study (0.37%), and no death occurred as a result of inadequate myocardial protection or communication failures. CONCLUSION: We postulate that the regular study of such events in all institutions may unmask systemic errors that can be remedied to prevent or reduce future occurrences. We encourage all units to use this methodology to detect any similarly modifiable factors in their practice.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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 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".