Rationale and Use of Perfusion Variables in the 2010 Update of the Society of Thoracic Surgeons Congenital Heart Surgery Database
Bibliographic record
Abstract
Patients undergoing congenital heart surgery are at risk of morbidity and mortality. The reasons underlying this risk are complex. To identify opportunities to reduce adverse sequelae, the cardiovascular perfusion community was invited to amend existing perfusion-related fields as well as add new ones to the current version of the Society of Thoracic Surgeons Congenital Heart Surgery Database (STS-CHSD). The International Consortium for Evidence-Based Perfusion (ICEBP) was invited by the STS-CHSD Task Force to identify and resolve ambiguities related to definitions among the 3 current perfusion-related fields as well as to propose new variables (and definitions) for inclusion in the 2010 update of the STS-CHSD. The ICEBP used teleconferences, wiki-based communication software, and e-mail to discuss current definitions and create new fields with definitions. The ICEBP created modified definitions to existing fields related to cardiovascular perfusion and also developed and defined new fields that focus on (1) techniques of circulatory arrest and cerebral perfusion, (2) strategies of myocardial protection, and (3) techniques to minimize hemodilution and allogeneic blood transfusions. Three fields in the STS-CHSD related to perfusion were redefined, and 23 new variables and definitions were selected for inclusion. Identifying and defining fields specific to the practice of perfusion are requisite for assessing and subsequently improving the care provided to patients undergoing congenital heart surgery. The article describes the methods and justification for adjudicating extant and new perfusion-related fields added to the 2010 update of the STS-CHSD.
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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 | 0.164 | 0.352 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".