Can Subspecialty Cancer Consultations Be Delivered to Communities Using Modern Technology?—A Pilot Study
Bibliographic record
Abstract
The objective of this project was to evaluate patient and physician acceptance of subspecialty oncologic teleconsultation for distant communities. Many newly diagnosed cancer patients have to travel several hours and long distances to attend specialty medical oncology consultations at our regional cancer center in Victoria, BC. Difficulties in recruiting of oncologists in Vancouver Island have prompted the search for other means to deliver subspecialty consultation closer to home. Teleconsultation seemed a possible model. Hence, 30 sequential patients with gastrointestinal (GI) malignancy referred from the Central Island region were seen after an informed consent via videoconferencing and 30 sequential patients were seen face to face in Victoria by one oncologist. Patients and the oncologist filled out a satisfaction questionnaire. The age, sex, proportion of patients who subsequently received chemotherapy, and the number of other co-morbid conditions were similar in both groups. No difference was observed in patient satisfaction whether patients were seen via videoconference or in person. However, the oncologist felt the video did not go as well as face-to-face consultation. Patients were very satisfied with teleconsultation, and it saved them hours of travel.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".