Development of a Multidisciplinary, Multicampus Subspecialty Practice in Endocrine Cancers
Bibliographic record
Abstract
OBJECTIVES: Relative to more abundant neoplasms, endocrine cancers have been historically neglected, yet their incidence is increasing. We therefore sought to build interest in endocrine cancers, improve physician experience, and develop innovative approaches to treating patients with these neoplasms. METHODS: Between 2005 and 2010, we developed a multidisciplinary Endocrine Malignancies Disease Oriented Group involving all 3 Mayo Clinic campuses (Rochester, Minnesota; Jacksonville, Florida; and Scottsdale, Arizona). In response to higher demand at the Rochester campus, we sought to develop a Subspecialty Tumor Group and an Endocrine Malignancies Tumor Clinic within the Division of Medical Oncology. RESULTS: The intended groups were successfully formed. We experienced difficulty in integration of the Mayo Scottsdale campus resulting from local uncertainty as to whether patient volumes would be sufficient to sustain the effort at that campus and difficulty in developing enthusiasm among clinicians otherwise engaged in a busy clinical practice. But these obstacles were ultimately overcome. In addition, with respect to the newly formed medical oncology subspecialty endocrine malignancies group, appointment volumes quadrupled within the first year and increased 7 times within 2 years. The number of active therapeutic endocrine malignancies clinical trials also increased from 1 in 2005 to 5 in 2009, with all 3 Mayo campuses participating. CONCLUSIONS: The development of subspecialty tumor groups for uncommon malignancies represents an effective approach to building experience, increasing patient volumes and referrals, and fostering development of increased therapeutic options and clinical trials for patients afflicted with otherwise historically neglected 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".