Feasibility study of multidisciplinary oncology rounds by videoconference for surgeons in remote locales
Bibliographic record
Abstract
BACKGROUND: This study was undertaken to assess the feasibility of using videoconferencing to involve community-based surgeons in interactive, multidisciplinary oncology rounds so they may benefit from the type of community of practice that is usually only available in academic cancer centres. METHODS: An existing videoconference service provider with sites across Ontario was chosen and the series was accredited. Indirect needs assessment involved examining responses to a previously conducted survey of provincial surgeons; interviewing three cancer surgeons from different regions of Ontario; and by analyzing an online portfolio of self-directed learning projects. Direct needs assessment involved a survey of surgeons at videoconference-enabled sites. A surgical, medical and radiation oncologist plus a facilitator were scheduled to guide discussion for each session. A patient scenario developed by the discussants was distributed to participants one week prior to each session. RESULTS: Direct and indirect needs assessment confirmed that breast cancer and colorectal cancer topics were of greatest importance to community surgeons. Six one-hour sessions were offered (two breast, two colorectal, one gynecologic and one lung cancer). A median of 22 physicians and a median of eight sites participated in each session. The majority of respondents were satisfied with the videoconference format, presenters and content. Many noted that discussion prompted reflection on practice and that current practice would change. CONCLUSIONS: This pilot study demonstrated that it is possible to engage remote surgeons in multidisciplinary oncology rounds by videoconference. Continued assessment of videoconferencing is warranted but further research is required to develop frameworks by which to evaluate the benefits of telehealth initiatives.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".