Implementation of malignant bowel obstruction multidisciplinary case conferences (MCCs) to improve clinical decision making in malignant bowel obstruction (MBO) in gynecologic oncology.
Bibliographic record
Abstract
e18322 Background: Though patients (pts) with gynecological cancer are at higher risk of MBO, clinical management is not well defined. We implemented a coordinated team approach to evaluate MBO at Princess Margaret Cancer Centre. The Princess Margaret Cancer Centre inter-professional MBO management program includes nurse led ambulatory symptom management, inpatient treatment algorithm, patient directed bowel management education & MCCs. This study evaluates the utility of MBO MCC on clinical decision making in gynecologic oncology. Methods: Monthly MBO MCCs are conducted to discuss complex clinical management issues. A clinical summary is presented prior to the discussion with each case incorporating radiology review followed by interdisciplinary discussion. In this study, the initial management plan was compared to post-MCC consensus. A change in plan was defined as a consensus plan different from the pre-MCC plan or no definite plan prior to MCC. Barriers to implementation of the consensus were analyzed. Results: From December 2016 to November 2018, 90 pts were discussed in 22 MCCs. Of these, 60 had high grade serous ovarian carcinoma (67%) & 64 had small bowel obstruction (71%). Discussion in MCCs lead to a change in management plan in 49 cases(54%). These changes included recommendations for palliative surgery (25%) or radiation (10%), interventional radiology (23%), pharmacologic management alone (14%), imaging studies (4%) & total parenteral nutrition (TPN) (4%). Chemotherapy continuation, break or regimen changes were recommended in 20%. MCC consensus plan could not be implemented in 11 cases (23%). The barriers were refusal of surgery (8%), interventional radiology procedures (2%), TPN (4%) by patients, functional decline (6%) & inability to create a colostomy due to dense adhesions (2%). During MCC referrals to the dietitian & palliative care team were planned for 16 (18%) & 22 (24%) pts respectively. Conclusions: Interdisciplinary MBO MCCs have a significant impact on decision making in complex MBO cases. Radiology review & group discussion facilitates greater clarity in formulation of a management plan.
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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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".