Novel malignant bowel obstruction (MBO) management program for women with advanced gynecological cancer.
Bibliographic record
Abstract
158 Background: Malignant bowel obstruction (MBO) is a common and challenging clinical predicament in women with advanced gynecological cancers. However, there is a lack of evidence-based guidelines or innovative approaches to improve patient care and quality of life. We implemented an inter-professional MBO management program incorporating a nurse-led ambulatory symptom management algorithm and multidisciplinary care conferences (MCC) as hallmarks of this program. Methods: Princess Margaret Cancer Centre has piloted an inter-professional MBO management program that supports women with advanced gynecological cancers who are at risk of/have developed MBO. The MBO team includes oncologists (medical, surgical, gynecologic and radiation), palliative care physicians, diagnostic and interventional radiologists, home parenteral nutrition physicians, specialized oncology nurses, dietitians, pharmacists and social workers. Complex MBO cases are discussed at regular MCC to derive treatment consensus. A symptom-driven MBO management algorithm has been devised and all patients are educated with a personalized bowel symptom management and dietary plan. For outpatient care, patients with MBO are proactively monitored by our specialized oncology nurses via phone or an eHealth bowel application to facilitate communication of symptoms and early intervention. Access to community services and home palliative care services are utilized to support care at home. All patients are enrolled into a prospective database to assess care impact and quality. Results: A total of 145 patients have been followed through the MBO management program over 12 months. At time of data cutoff, 14 had MBO (3 inpatients and 11 outpatients) and 22 were deemed at risk of MBO. Majority patients are managed as an outpatient and avoided unnecessary emergency department episodes. Detailed methodology and data analyses will be presented. Conclusions: A successful novel MBO program incorporating inter-professional care model and nurse-led ambulatory symptom management algorithm optimizes patient care in this vulnerable population and foster collaboration in implementing best practice clinical processes.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".