Older Age Associated With Quality Of Rectal Cancer Care: An ACS NSQIP Database Study
Bibliographic record
Abstract
Introduction: Rectal cancer operation requires adherence to specific multidisciplinary preoperative, intraoperative, and postoperative elements of care. Compliance with all six preoperative elements available in the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP)’s database has been associated with better surgical and oncologic outcomes. This study aimed to identify sociodemographic factors associated with rectal cancer checklist compliance. Methods: This is a retrospective cohort study of adult rectal cancer patients who underwent elective operation in the ACS-NSQIP database between 2016-2019. The main outcome was compliance with all six preoperative checklist elements (complete evaluation of colon, pretreatment tumour location, pretreatment locoregional and distant staging, appropriate use of neoadjuvant radiotherapy, preoperative stoma marking). Multiple logistic regression was used to identify sociodemographic factors associated with checklist compliance while accounting for relevant clinical factors. Results: A total of 5,428 patients met inclusion criteria and only 22.6% (n=1,228) were compliant with all six checklist items. Elderly patients (≥70 years, n=1,545) had significantly lower compliance (18.1% vs 24.4%) than those aged <70 years (p=<0.0001). Compliance with individual checklist items in this group was lowest for neoadjuvant radiation therapy for ≥T3 or node-positive disease (25.3%), and pretreatment locoregional and distant staging (65.2% and 53.4%, respectively). Multiple logistic regression estimated a 2% decrease in odds of checklist compliance with every year increase in age (OR=0.977, 95%CI=0.969-0.984). Conclusion: Elderly (≥70 years) patients require more attention when it comes to preoperative investigations and interventions prior to rectal cancer operation, specifically appropriate preoperative staging.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".