Bibliographic record
Abstract
To review the current literature evaluating the performance of the Surgical Apgar Score (SAS). Background: The SAS is a simple metric calculated at the end of surgery that provides clinicians with information about a patient's postoperative risk of morbidity and mortality. The SAS differs from other prognostic models in that it is calculated from intraoperative rather than preoperative parameters. The SAS was originally derived and validated in a general and vascular surgery population. Since its inception, it has been evaluated in many other surgical disciplines, large heterogeneous surgical populations, and various countries. Methods: A database and gray literature search was performed on March 3, 2020. Identified articles were reviewed for applicability and study quality with prespecified inclusion criteria, exclusion criteria, and quality requirements. Thirty-six observational studies are included for review. Data were systematically extracted and tabulated independently and in duplicate by two investigators with differences resolved by consensus. Results: All 36 included studies reported metrics of discrimination. When using the SAS to correctly identify postoperative morbidity, the area under the receiver operating characteristic curve or concordance-statistic ranged from 0.59 in a general orthopedic surgery population to 0.872 in an orthopedic spine surgery population. When using the SAS to identify mortality, the area under the receiver operating characteristic curve or concordance-statistic ranged from 0.63 in a combined surgical population to 0.92 in a general and vascular surgery population. Conclusions: The SAS provides a moderate and consistent degree of discrimination for postoperative morbidity and mortality across multiple surgical disciplines.
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.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".