Management of Malignant Bowel Obstruction
Bibliographic record
Abstract
BACKGROUND: Malignant bowel obstruction (MBO) in patients with advanced gynecologic cancer (GyCa) can negatively impact clinical outcomes and quality of life. Oncology nurses can support these patients with adequate tools/processes. PROBLEM: Patients with GyCa with/at risk of MBO endure frequent emergency or hospital admissions, impacting patient care. APPROACH: Optimizing oncology nurses' role to improve care for patients with GyCa with/at risk of MBO, the gynecology oncology interprofessional team collaborated to develop a proactive outpatient nurse-led MBO model of care (MOC). OUTCOMES: The MBO MOC involves a risk-based algorithm engaging interdisciplinary care, utilizing standardized tools, risk-based assessment, management, and education for patients and nurses. The MOC has improved patient-reported confidence level of bowel self-management and decreased hospitalization. Following education, nurses demonstrated increased knowledge in MBO management. CONCLUSIONS: An outpatient nurse-led MBO MOC can improve patient care and may be extended to other cancer centers, fostering collaboration and best practice.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".