Disrupting the paradigm of oncology care in Canada: the triumphs, challenges and future implications of telemedicine
Bibliographic record
Abstract
Medicine has long been one of the lingering aspects of society yet to be fully disrupted by technological advances. Unlike media, banking and commerce which have adapted to the growing demand for convenience and accessibility from the public, the practice of medicine in many ways remains much unchanged from decades prior. The 2019 novel coronavirus (COVID-19) demanded an immediate shift in the way Canadian healthcare was delivered to reduce the risk of viral transmission from in person patient encounters. Cancer poses a large and ever-increasing impact on the Canadian population and healthcare resources. Brenner et al. (2020) estimated nearly half of the Canadian population will develop cancer in their lifetime in addition to the recent increasing yearly number of new diagnoses and deaths as the population grows and ages [1]. Cancer patients were initially an ideal population for telemedicine encounters during the pandemic. These patients often have additional comorbidities association with COVID-19 mortality and a diagnosis of cancer may further increase this risk [2]. As healthcare enters a second year within the new paradigm of virtual medicine, it is important to consider the impact and future of telemedicine on Canada’s ever-growing oncology patients.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 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".