Making National Cancer Institute–Designated Comprehensive Cancer Center Knowledge Accessible to Community Oncologists via an Online Tumor Board: Longitudinal Observational Study
Bibliographic record
Abstract
BACKGROUND: Expert knowledge is often shared among multidisciplinary academic teams at tumor boards (TBs) across the country, but these conversations exist in silos and do not reach the wider oncology community. OBJECTIVE: Using an oncologist-only question and answer (Q&A) website, we sought to document expert insights from TBs at National Cancer Institute-designated Comprehensive Cancer Centers (NCI-CCCs) to provide educational benefits to the oncology community. METHODS: We designed a process with the NCI-CCCs to document and share discussions from the TBs focused on areas of practice variation on theMednet, an interactive Q&A website of over 13,000 US oncologists. The faculty translated the TB discussions into concise, non-case-based Q&As on theMednet. Answers were peer reviewed and disseminated in email newsletters to registered oncologists. Reach and engagement were measured. Following each Q&A, a survey question asked how the TB Q&As impacted the readers' practice. RESULTS: A total of 23 breast, thoracic, gastrointestinal, and genitourinary programs from 16 NCI-CCC sites participated. Between December 2016 and July 2021, the faculty highlighted 368 questions from their TBs. Q&As were viewed 147,661 times by 7381 oncologists at 3515 institutions from all 50 states. A total of 277 (75%) Q&As were viewed every month. Of the 1063 responses to a survey question on how the Q&A affected clinicians' practices, 646 (61%) reported that it confirmed their current practice, 163 (20%) indicated that a Q&A would change their future practice, and 214 (15%) reported learning something new. CONCLUSIONS: Through an online Q&A platform, academics at the NCI-CCCs share knowledge outside the walls of academia with oncologists across the United States. Access to up-to-date expert knowledge can reassure clinicians' practices, significantly impact patient care in community practices, and be a source of new knowledge and education.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".