Prognostic value of the Duke Activity Status Index (DASI) in patients undergoing colorectal surgery
Bibliographic record
Abstract
BACKGROUND: Complications are common after colorectal surgery and remain a target for quality improvement. Lower preoperative physical functioning is associated with poor postoperative outcomes, but assessment often relies on subjective judgment or resource-intensive tests. Recent literature suggests that self-reported functional capacity, measured using the Duke Activity Status Index (DASI), is a strong predictor of postoperative outcomes. This study aimed to estimate the extent to which DASI predicts 30-day complications after colorectal surgery. METHODS: In this observational study, 100 patients undergoing colorectal resection [median age 63, 57% men, 81% laparoscopic, 37% rectal surgery] responded to DASI two weeks preoperatively. Complications were classified according to Clavien-Dindo and quantified using the comprehensive complication index (CCI). Our primary analysis targeted the relationship between preoperative DASI and odds of complications. Secondary analyses focused on 30-day severe complications, CCI, readmissions, and length of stay (LOS). We also explored the predictive ability of DASI with scores dichotomized based on a previously validated threshold (≤ 34). RESULTS: Mean preoperative DASI was 48 ± 12. Forty-six patients (46%) experienced 30-day complications (8% severe, CCI 9.6 ± 15). Lower DASI scores were associated with higher odds of complications (OR 1.08, 95%CI 1.03-1.14; p = 0.001). Preoperative DASI was also an independent predictor of severe complications, CCI, and readmissions. The predictive ability was supported when scores were dichotomized at ≤ 34. CONCLUSION: DASI is a significant predictor of postoperative complications after colorectal surgery. This questionnaire can be easily implemented in clinical practice to identify patients with low preoperative functional capacity and target interventions to those at higher risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".