Risk-stratified multidisciplinary ambulatory management of malignant bowel obstruction (MAMBO) program for women with advanced gynecological cancer.
Bibliographic record
Abstract
e18024 Background: Malignant bowel obstruction (MBO) in gynecologic oncology patients is associated with poor prognosis, debilitating symptoms and compromises quality of life. Management of MBO poses a clinical challenge with prolonged hospitalization. Evidence based guidelines for surgical intervention, use of chemotherapy, total parenteral nutrition or best supportive care in this patient population is lacking. Surgical correction may improve survival in selected patients. Retrospective analysis to assess impact of MBO show variable range of MBO-related admissions up to 60 days, and is associated with significant morbidity. Methods: A risk stratified MAMBO program for gynecologic patients has been implemented at Princess Margaret Cancer Centre to define a systematic approach for MBO management and build multidisciplinary consensus for personalized treatment of our patients. The program is novel and includes a nurse-led ambulatory management algorithm with an eHealth application designed to monitor bowel symptoms. A symptom-driven classification system has been devised to objectively define risk using a MBO management algorithm. Complex MBO cases are discussed in designated MBO rounds for consensus treatment recommendation. All patients with MBO are enrolled into a prospective database. Patients undergoing surgical procedures for MBO are consented for opportunistic tissue collection for translational research. MBO patient education materials have been developed to improve awareness and encourage proactive bowel symptom management. Results: Seventy nine patients have been followed through this risk stratified MAMBO program for ambulatory care over 6 months. The MBO program integrates diet, laxatives/stool softeners and drug therapy. Designated MBO rounds are now established for complex case discussion. A prospective MBO database will evaluate treatment and patient-reported outcomes. Conclusions: Risk stratified model of care for multidisciplinary MBO program facilitates decision-making between disciplines and optimize patient care in a vulnerable population with support for ambulatory care.
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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 | 0.000 | 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".