Effect of Preoperative Geriatric Evaluation on Outcomes After Elective Surgery: A Population‐Based Study
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Randomized and nonrandomized single-center studies suggest that preoperative geriatric evaluation improves postoperative outcomes in older adults. The generalizability and population-level effect of preoperative geriatric evaluation has not been determined. Our objective was to measure the adjusted association between preoperative geriatric evaluation and postoperative outcomes. DESIGN: Multilevel multivariable regression model analysis of a population-based historical cohort. SETTING: Publicly funded universal healthcare system in Ontario, Canada. PARTICIPANTS: All adults aged 65 and older having major, elective, noncardiac surgery from 2002 to 2014 (N = 266,499). INTERVENTION: We studied geriatric consultations and comprehensive assessments performed in the 4 months prior to surgery. These were identified using validated methods. MEASUREMENTS: Ninety-day survival (primary outcome), in-hospital complications, length of stay, 30-day readmissions, need for supported discharge, and 90-day costs of care. RESULTS: The 7,352 participants (2.8%) who had a preoperative geriatric evaluation had longer 90-day survival than those who who did not (adjusted hazard ratio = 0.81, 95% confidence interval = 0.68-0.95). Length of stay and complication rates did not differ between groups, but participants evaluated by a geriatrician preoperatively had higher rates of supported discharge, readmission rates, and costs of care. Sensitivity analyses supported the association between preoperative geriatric assessment and 90-day survival. CONCLUSION: In individuals aged 65 and older undergoing major, elective, noncardiac surgery, preoperative geriatric evaluation was associated with longer 90-day survival, but it is used infrequently. Given these results, and those of previous small studies, the influence of a geriatric evaluation on postoperative outcomes should be determined in a multicenter randomized trial.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".