Actionable adverse event monitoring and feedback to improve thoracic surgical care
Bibliographic record
Abstract
Abstract: Postoperative adverse events (AEs) pose a major hurdle in improving the cost and quality of care administered across the thoracic surgical discipline. Although hospital-based mortality following thoracic surgery has decreased significantly in the last few decades, AE rates have remained common at a frequency of 30–60% depending on the specific surgery performed. AEs are not only associated with diminished patient outcomes and quality of life, but also impose massive economic costs to the healthcare system, with literature estimates indicating a $4,000–44,000-increase in treatment expenses per patient per AE. Thus, there exists a great demand on several fronts for systematic, scalable, and effective methods to address AEs and improve the quality of care administered by providers. In this narrative review, we explore how designing data-driven actionable AE monitoring and feedback systems can allow for sustained improvement and standardization of thoracic surgical care. We highlight existing AE classifications to monitor AE occurrences, namely the Clavien-Dindo classification and the Thoracic Morbidity and Mortality (TM&M) system, and means to harmonize AE classification and data entry across the major AE classification systems. Additionally, we explore three AE feedback methodologies intended to lead to action: (I) actionable Morbidity and Mortality (M&M) rounds, (II) positive deviance (PD) seminars, and (III) benchmarking. These methodologies provide distinct yet complementary avenues for quality improvement within thoracic surgery within local, regional, and international settings, and provide frameworks for broader collaboration and coordination, and the emergence of AE monitoring and feedback networks between institutions of care.
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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 | 0.070 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".