Leveraging Nursing Assessment for Early Identification of Post Operative Gastrointestinal Dysfunction (POGD) in Patients Undergoing Colorectal Surgery
Bibliographic record
Abstract
Background: Postoperative gastrointestinal dysfunction (POGD) remains a common morbidity after gastrointestinal surgery. POGD is associated with delayed hospital recovery, increased length of stay, poor patient satisfaction and experience, and increased economic hardship. The I-FEED scoring system was created by a group of experts to address the lack of a consistent objective definition of POGD. However, the I-FEED tool needs clinical validation before it can be adopted into clinical practice. The scope of this phase 1 Quality Improvement initiative involves the feasibility of implementing percussion into the nursing workflow without additional burden. Methods: All gastrointestinal/colorectal surgical unit registered nurses underwent comprehensive training in abdominal percussion. This involved understanding the technique, its application in postoperative gastrointestinal dysfunction assessment, and its integration into the existing nursing documentation in the Electronic Health Record (EHR). After six months of education and practice, a six-question survey was sent to all inpatient GI surgical unit nurses about incorporating the percussion assessment into their routine workflow and documentation. Results: Responses were received from 91% of day-shift nurses and 76% of night-shift registered nurses. Overall, 95% of the nurses were confident in completing the abdominal percussion during their daily assessment. Conclusion: Nurses’ effective use of the I-FEED tool may help improve patient outcomes after surgery. The tool could also be an effective instrument for the early identification of postoperative gastrointestinal dysfunction (POGD) in surgical patients.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".