The CR-POSSUM Risk Calculator Predicts Failure of Enhanced Recovery after Colorectal Surgery
Bibliographic record
Abstract
AIMS: To determine predictors of failed enhanced recovery after surgery (ERAS) in patients after elective colorectal surgery. METHODS: A cohort of 55 patients undergoing elective colorectal surgery was monitored prospectively. Perioperative care was based on a previously established protocol for ERAS. Pre-, intra-, and postoperative parameters were analyzed to elicit predictors of ERAS failure. ERAS failure was defined as prolonged hospital stay (> 7 days). The risk calculator CR-POSSUM was evaluated for its clinical utility. RESULTS: Body mass index (BMI) or the American Society of Anesthesiologists score (ASA) was not associated with ERAS failure on univariate analysis, but patients that failed ERAS were significantly older (64 y vs 54 y; p = 0.023). Prolonged length of stay (>7 days) was also associated with an open approach (p = 0.009), intraoperative nasogastric tube placement (p = 0.005), blood loss > 500 ml (p = 0.008), stoma formation (p = 0.006) and insertion of more than one intraabdominal drain during surgery (p = 0.005). Postoperative continuation of intravenous fluids (p = 0.027), reinsertion of urinary catheter (p = 0.045) and postoperative ileus (p = 0.020) were also strongly associated with delayed discharge on univariate analysis. After multivariate analysis the preoperative parameters CR-POSSUM score (p = 0.022), increasing BMI (p = 0.014) and preoperative albumin level (p = 0.031) were all independently associated with failure of ERAS. CONCLUSIONS: A variety of perioperative factors contribute to failure of ERAS in routine practice. CR-POSSUM can help to identify patients at risk for possible failure of ERAS. This may help to optimize avoidable factors, or accommodate those patients likely to require a longer post-operative stay.
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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) | 0.001 | 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".