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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".