Bibliographic record
Abstract
Want to get oncology care to a small population spread over a large area? Canada is a case in point of a country rising to that challenge. In Canada, with a land mass spanning more than 3.8 million square miles and a population of only 34 million tucked mainly into a few urban centers, telemedicine is just about the only economically feasible option for delivering quality health care to the hundreds of small communities scattered throughout its northern regions. (By contrast, the U.S. has a population of 314 million people on 3.7 million square miles.) Perhaps not surprisingly, Canada has spawned one of the world’s largest telemedicine networks, and oncology is a big part of that. The easternmost point of continental North America is Cape Spear, in the province of Newfoundland and Labrador, a short drive from the provincial capital of St. John’s. From there, it’s water all the way to Ireland, where most of the province’s inhabitants trace their roots. One of the country’s largest provinces, Newfoundland and Labrador, also has its lowest population density, with only about four people per square mile and almost everyone located in or near the capital. The region also consists of two landmasses accessible to each other only via ferry, making travel a challenge. That isolation has pressed the region’s only academic medical center, at Memorial University in St. John’s, to provide distance learning and tele-health as a means of servicing its remote communities long before the notion was even considered elsewhere.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".