Harmonization of adverse events monitoring following thoracic surgery: Pursuit of a common language and methodology
Bibliographic record
Abstract
Objective: Thoracic surgery carries significant risk of postoperative adverse events (AEs). Multiple international recording systems are used to define and collect AEs following thoracic surgery procedures. We hypothesized that a simple-yet-ubiquitous approach to AE documentation could be developed to allow universal data entry into separate international databases. Methods: AE definitions of the Canadian Association of Thoracic Surgeons (CATS) system and 4 international databases were matched and compared. This consisted of reviewing the definition of each AE as described by their respective database and assessing compatibility with the CATS system. We developed a single set of 4 drop-down menus to enable clear classification and facilitated data entry, using 3 single-select mandatory lists and 1 multiselect optional list classifying type and severity of these events. Results: The CATS data elements were harmonized (ie, perfect or good) with 100% (European Society of Thoracic Surgeons), 89% (Society of Thoracic Surgeons), 74% (Esophagectomy Complications Consensus Group), and 73% (National Surgical Quality Improvement Program) of respective data elements. The addition of 17 AEs and 2 complication modifiers to the CATS system was implemented to achieve complete harmonization. Consequently, 100% of AE data elements currently included in all 4 international databases are perfectly or well-harmonized with the revised 4-choice drop down menu. Conclusions: We describe a framework for a ubiquitously applicable approach to AE monitoring following thoracic surgery harmonized with AE definitions of all major thoracic international associations. Use of this AE collection framework allows for comprehensive evaluation of both the incidence and severity of all AEs after thoracic surgery along with quality indicators.
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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 | 0.292 | 0.303 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".