A Survey Assessment of Nuclear Medicine Trainees’ Participation and Impact in Multidisciplinary Cancer Conferences: A Single-Center Study
Bibliographic record
Abstract
BACKGROUND: Multidisciplinary cancer conferences (MCCs) are essential forums for collaborative oncology decision-making. However, existing literature has primarily examined the role of attending specialists and has rarely differentiated effects by specialty. The contributions of trainees, particularly in nuclear medicine, have been largely overlooked, leaving a gap in understanding how their participation influences both educational outcomes and patient management. This study addresses this gap by systematically evaluating the perceived impact of nuclear medicine trainees in MCCs. METHODS: A cross-sectional survey was distributed to 73 healthcare professionals at a tertiary medical center, including nuclear medicine specialists, trainees, and clinicians from surgery, oncology, and radiology. The survey included Likert-scale and multiple-choice questions to assess perceptions of trainee contributions to interprofessional collaboration, clinical decision-making, and patient outcomes. Descriptive statistics were calculated, and analysis of variance (ANOVA) and chi-square tests were applied to analyze Likert-scale responses and compare responses between nuclear medicine and non-nuclear medicine specialists. P-values < 0.05 were considered statistically significant. RESULTS: Of the 73 respondents, 57 (78.1%) indicated that nuclear medicine trainees enhanced interprofessional collaboration, while 56 (76.7%) reported a positive influence on patient care. Additionally, 60 (82.2%) perceived an educational benefit through enriched clinical knowledge. Chi-square analysis revealed no significant differences in perceptions across professional groups (p = 0.568). Reported barriers included inconsistent attendance, limited clinical experience, and time constraints. CONCLUSION: Nuclear medicine trainees play a valuable role in MCCs by enriching clinical discussions, supporting patient care, and contributing to professional development. To maximize their impact, structured learning opportunities, increased mentorship, and improved logistical support are recommended. These findings emphasize the importance of formally integrating trainees into MCC workflows to enhance both educational and clinical outcomes.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".