International approaches to head and neck cancer multidisciplinary tumor board.
Bibliographic record
Abstract
e18615 Background: Multidisciplinary tumor boards are widely accepted as vehicles for improving patient outcomes in Head and Neck Cancer (HNC). Little work describes their structure and practices. The purpose of this study is to better understand the international practice patterns of multidisciplinary HNC tumor boards. Methods: A cross-sectional survey on head and neck cancer multidisciplinary tumor board practice patterns was developed by a panel of six experts and distributed internationally to HNC providers. The survey interrogated the attendance, participation, operation, and perceptions of multidisciplinary tumor conferences, through a mix of Likert-based, tick box and open-ended questions. Results: One hundred and twenty-three responses (55%) were received from 88 surgical oncologists, 17 radiation oncologists, and 18 medical oncologists from nine different countries. Overall, most HNC tumor boards are led by a surgeon (77%), and most commonly 5-10 minutes (61%) was spent on each case. In 60% of responses, all HNC patients were discussed at their tumor boards, while select cases were presented in 40% of responses. Pathology was routinely reviewed in 75% of sites and imaging reviewed in 95% of sites. In 75% of responses, sufficient time was felt to be spent on each case. Majority (75%) of tumor boards documented their recommendation, with 92% reporting that inability to reach a consensus recommendation was rare. When this occurred, the most common recourse was involving patient decision making (53%), followed by offline discussion until an agreement is reached (38%). Most respondents felt that tumor boards rarely altered the treatment plan (68%), while 37% felt the treatment plan was sometimes altered. Involvement of radiation and medical oncology prior to surgery varied, with 53% sending patients routinely, 32% sometimes, and 15% deferring referral. Logistics was cited as a primary barrier. Surgeons and radiation oncologists agree that the top three reasons tumor boards assist in cancer care are: receiving additional opinion and perspective, coordinating care, and communication. Medical oncologist also found tumor boards enhance clinical trial enrollment. Conclusions: While there are variations in the structure and process of multidisciplinary tumor boards, the majority of management is agreed upon by the treatment team. Areas of improvement include verification of cancer stage, identifying logistics that prevent timely and documentation of recommendations. Identifying the variations from most-common practice should provide a mechanism for improvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.003 |
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".