A Review of Canadian Cancer-Related Clinical Practice Guidelines and Resources during the COVID-19 Pandemic
Bibliographic record
Abstract
(1) Background: Preventive measures taken in response to the coronavirus disease 2019 (COVID-19) pandemic have adversely affected an entire range of cancer-related medical activities. The reallocation of medical resources, staff, and ambulatory services, as well as critical shortages in pharmaceutical and medical supplies have compelled healthcare professionals to prioritize patients with cancer to treatment and screening services based on a set of classification criteria in cancer-related guidelines. Cancer patients themselves have been affected on multiple levels, and addressing their concerns poses another challenge to the oncology community. (2) Methods: We conducted a Canada-wide search of cancer-related clinical practice guidelines on the management and prioritization of individuals into treatment and screening services. We also outlined the resources provided by Canadian cancer charities and patient advocacy groups to provide cancer patients, or potential cancer patients, with useful information and valuable support resources. (3) Results: The identified provincial guidelines emphasized cancer care (i.e., treatment) more than cancer control (i.e., screening). For cancer-related resources, a clear significance was placed on knowledge & awareness and supportive resources, mainly relating to mental health. (4) Conclusion: We provided a guidance document outlining cancer-related guidelines and resources that are available to healthcare providers and patients across Canada during the COVID-19 pandemic.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".