Frail Patients Undergoing Optimization Before Surgery: Preliminary Results
Bibliographic record
Abstract
BACKGROUND: It is estimated that 10% or more of patients older than 65 years are affected by frailty, a mental and physical state of vulnerability to adverse surgical outcomes. Frailty can be assessed using the Edmonton Frailty Scale: a reliable and convenient multidimensional assessment before surgery. The correlation between frailty score, presurgical optimization, and surgical outcomes was investigated in this preliminary pilot study. STUDY DESIGN: A retrospective study was performed on patients referred to the surgical optimization clinic and assessed for frailty from September 2020 to May 2023. Patients received presurgical optimization for reasons including diabetes, smoking cessation, prehabilitation and nutrition, and/or cardiopulmonary issues. Outcomes were evaluated whether they proceeded to surgery, were referred to the High-Risk Surgical Committee, surgical case canceled, or not scheduled. For those who proceeded to surgery, infection rates, complications, and 30-day emergency department (ED) and readmission rates were evaluated. RESULTS: Of 143 unique patients, 138 (men = 61, women = 77) were evaluated for this study. The average Edmonton frailty score for patients who proceeded to surgery was 7.013 (n = 78) vs 9.389 with cancelation and 9.600 for not scheduled or not optimized for surgery. Postoperative infection rates were <3%. However, 30-day ED and readmission rate was 21% (16 of 78). CONCLUSIONS: Patients with lower average Edmonton frailty scores were more likely to proceed to surgery, whereas those with higher average Edmonton frailty scores were more likely to have surgery canceled or delayed. Frail patients cleared for surgery were found to have a high 30-day ED and readmission rate.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".