Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A wide variety of risk stratification systems have been developed to quantify the risk of cardiac surgery. Generally, the focus has been on mortality; however, more recently models have been developed that allow the preoperative prediction of the incidence of morbidity, including renal failure, infection, prolonged ventilation, and neurologic deficit. Many of these risk stratification models are developed from large databases of cardiac surgical patients. Patient and surgical factors that are present preoperatively are assessed for their predictive value for postoperative complications. Risk factors that are found to be significant are assigned a specific weight in the overall summation of risk. These models have been used as tools to compare surgeon's results, institutional outcomes, individual patient risk, and within quality improvement programs. This article will focus on the European System for Cardiac Operative Risk Evaluation, the Society of Thoracic Surgeons score, the Parsonnet score, Cleveland Clinic Model, the Bayes model, and the Northern New England Score.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it