Pan-Canadian standards for cancer surgery
Bibliographic record
Abstract
About the Canadian Partnership Against Cancer: The Canadian Partnership Against Cancer (CPAC) is an independent organization funded by the federal government to accelerate action on cancer control for all Canadians. As the steward of the Canadian Strategy for Cancer Control (the Strategy), the Partnership works with Canada’s cancer community to take action to ensure fewer people get cancer, more people survive cancer and those living with the disease have a better quality of life. This work is guided by the Strategy, which was refreshed for 2019 to 2029, and will help drive measurable change for all Canadians affected by cancer. The Strategy includes 5 priorities that will tackle the most pressing challenges in cancer control as well as distinct First Nations, Inuit and Métis Peoples–specific priorities and actions reflecting Canada’s commitment to reconciliation. A specific action in the Strategy calls for reducing the differences in practice and service delivery by setting standards for high-quality care and promoting their adoption. The CPAC will oversee the implementation of the priorities in collaboration with organizations and individuals on the front lines of cancer care: the provinces and territories; health care professionals; people living with cancer and those who care for them; First Nations, Inuit and Métis communities; governments and organizations; and its funder, Health Canada. Learn more about the Partnership and the refreshed Strategy at www.cancerstrategy.ca.
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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.061 | 0.024 |
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".