Multidisciplinary Reference Centers: The Care of Neuroendocrine Tumors
Bibliographic record
Abstract
The purpose of this study was to review the need for and benefits of multidisciplinary care in patients with cancer, to describe our experience setting up a multidisciplinary reference center (MRC) dedicated to the treatment of the uncommon cancer neuroendocrine tumors (NETs), and to present the perspective of a patient seeking treatment at our center.The literature was searched to review the outcomes of patients with cancer treated by a multidisciplinary team.Multidisciplinary care for patients with more common cancers has been associated with improvements in diagnosis, treatment planning, survival, patient satisfaction, and clinician satisfaction. Similar benefits have been seen in patients with NETs receiving treatment at a specialty center. The establishment of our NETs MRC allows us to offer integrated care, providing surgical oncology and medical oncology disciplines; nurses well experienced in the treatment of NETs; and the expertise of endocrinology, diagnostic radiology, and interventional radiology specialists. Since our clinic was established, we have increased our availability to see patients and have received positive feedback from those attending.MRCs have been associated with improved patient outcomes. As providers at a dedicated NETs MRC, we feel that these centers have a positive effect on both patient and provider experience. The creation of specialty centers with a focus on improving outcomes and quality of care should be a goal of health care systems and are especially important for patients with NETs and other rare cancers.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".