Impact of head-and-neck radiation oncology clinical fellowship on multidisciplinary assessment, radiation workflow, and survival of adult patients with nasopharyngeal carcinoma
Bibliographic record
Abstract
Purpose: The purpose of the study wast to evaluate the influence of head-and-neck clinical fellowship training on multidisciplinary assessment, radiation workflow, and clinical outcomes of patients with nasopharyngeal carcinoma (NPC). Materials and Methods: This was a retrospective review of patients with NPC treated between 2010 and 2017. The study cohort was allocated into prefellowship cohort (pre-FSC) (January 2010-September 2014) and postfellowship cohort (post-FSC) (October 2014-December 2017). Patient demographics, tumor characteristics, multidisciplinary assessment, radiation workflow, and treatment were reviewed. Univariable, multivariable, and relapse-free survival (RFS) and overall survival (OS) were performed. Results: One hundred and forty-three patients were included, 68 in the pre-FSC and 75 in the post-FSC. For the post-FSC versus pre-FSC, there were increased multidisciplinary referrals to dental (100% vs. 79.4%, P = 0.001), nutritional (94.7% vs. 70.6%, P = 0.0001), peg-tube insertion (84% vs. 64.7%, P = 0.0001), speech and swallow (94.7% vs. 13.2%, P = 0.0001), psychosocial (100% vs. 26.5%, P = 0.0001), and smoking cessation clinic (33.3% vs. 5.9%, P = 0.0001). For the post-FSC versus pre-FSC, there were statistically significant differences in the elements of radiation workflow; mean time required for contouring was 3.2 vs. 8.8 days (P = 0.0001), radiotherapy plan implementation: 1.9 vs. 4.8 days (P = 0.0001), and plan approval: 0.4 vs. 0.9 day (P = 0.00012). On multivariable analysis, smoking was associated with poor RFS (P = 0.04). There were no statistically significant differences in OS (94.7% vs. 87.2% at 3 years; P = 0.126) and RFS (88.4% vs. 84.4% at 3 years; P = 0.281) between post and pre-FSCs, respectively. Conclusions: Clinical fellowship training results in increase multidisciplinary referrals to supportive services, improves radiotherapy workflow, but had no significant impact on survival outcomes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".