Improving Surgical Care and Outcomes in Older Cancer Patients Through Implementation of a Presurgical Toolkit (OPTI-Surg)—Final Results of a Phase III Cluster Randomized Trial (Alliance A231601CD)
Bibliographic record
Abstract
OBJECTIVE: To assess the effect of a practice-level preoperative frailty screening and optimization toolkit (OPTI-Surg) on postoperative functional recovery and complications in elderly cancer patients undergoing major surgery. BACKGROUND: Frailty is common in older adults. It increases the risk of poor postoperative functional recovery and complications. The potential for a practice-level screening/optimization intervention to improve outcomes is unknown. METHODS: Thoracic, gastrointestinal, and urologic oncological surgery practices within the National Cancer Institute Community Oncology Research Program (NCORP) were randomized 1:1:1 to usual care (UC), OPTI-Surg, or OPTI-Surg with an implementation coach. OPTI-Surg consisted of the Edmonton Frail Scale and guided recommendations for referral interventions. Patients 70 years old or above undergoing curative intent surgery were eligible. The primary outcome was 8 weeks postoperative function (kcal/wk). The key secondary outcome was complications within 90 days. Mixed models were used to compare UC to the 2 OPTI-Surg arms combined. RESULTS: From July 2019 to September 2022, 325 patients were enrolled in 29 practices. One hundred ninety-nine (64 UC, 135 OPTI-Surg) and 279 (78 UC, 201 OPTI-Surg) were evaluable for primary and secondary analysis, respectively. UC and OPTI-Surg patients did not significantly differ in total caloric expenditure (2.2 UC, 2.0 OPTI-Surg) after adjusting for baseline function ( P =0.53). UC and OPTI-Surg patients did not significantly differ in postoperative complications (25.6% UC, 35.3% OPTI-Surg, P =0.5). CONCLUSIONS: Frailty assessment was successfully performed, but the OPTI-Surg intervention did not improve postoperative function nor reduce postoperative complications compared with UC. Future analysis will explore practice-level factors associated with toolkit implementation and the differences between the coaching and noncoaching arms.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".