Multidisciplinary cancer conferences: Exploring the attitudes of cancer care providers and administrators
Bibliographic record
Abstract
The multidisciplinary cancer conference (MCC) provides an outlet for contributors in cancer care collectively to evaluate diagnosis and treatment options and to provide optimal patient care. The prevalence and perceived benefits of MCCs in Canada have not previously been described. Between February and March 2007, the Cancer Services Integration Survey, including four key statements concerning MCCs, was administered to cancer care providers and administrators in Ontario, Canada. A total of 1,769 responses were received with a response rate of 33%. Overall, 74% of respondents were aware of MCCs within their region, but only 58% were either regular MCC participants, or acknowledged participation of cancer providers in their institutions. Using multilevel modeling, physicians (OR 2.69, p-value < 0.01, 95% CI 1.62-4.57) and surgeons (OR 3.00, p-value < 0.01, 95% CI 1.52-6.20) both perceived greater benefit of MCCs for coordinating and improving patient plans than administrators. Although MCCs appear to positively influence patient care and interprofessional interactions, variability exists among cancer providers and administrators concerning their acceptance and perceived benefits. Further research should concentrate on further probing these trends, and exploring explanations and solutions for the inconsistent acceptance of MCCs into routine cancer 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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".