How can we identify the high-risk patient?
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
PURPOSE OF REVIEW: Accurate and early identification of high-risk surgical patients allows for targeted use of perioperative monitoring and interventions that may improve their outcomes. This review summarizes current evidence on how information from the preoperative, operative, and immediate postoperative periods can help identify such individuals. RECENT FINDINGS: Simple risk indices, such as the Revised Cardiac Risk Index or American Society of Anesthesiologists Physical Status scale, and online calculators allow risk to be estimated with moderate accuracy using readily available preoperative clinical information. Both specific specialized tests (i.e., cardiopulmonary exercise testing and cardiac stress testing) and promising novel biomarkers (i.e., troponins and natriuretic peptides) can help refine these risk estimates before surgery. Estimates of perioperative risk can be further informed by information acquired during the operative and immediate postoperative periods, such as risk indices (i.e., surgical Apgar score), individual risk factors (i.e., intraoperative hypotension), or postoperative biomarkers (i.e., troponins and natriuretic peptides). SUMMARY: Preoperative clinical risk indices and risk calculators estimate surgical risk with moderate accuracy. Although novel biomarkers, specialized preoperative testing, and immediate postoperative risk indices show promise as methods to refine these risk estimates, more research is needed on how best to integrate risk information from these different sources.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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