Global Conference Report: A brief report on the 2020 Canadian Global Oncology Workshop
Bibliographic record
Abstract
Cancer continues to be one of the leading causes of death worldwide, with the World Health Organization (WHO) reporting cancer as the first or second leading cause of death in 112 countries (WHO, 2020a). The American Cancer Society recently published statistics on global cancer, including an estimated incidence of 19.3 million new cancer cases and nearly 10 million deaths due to cancer in 2020 (Sung et al., 2021). Approximately 75% of cancer deaths occur in low- and middle-income countries (LMICs) and yet, only 5% of global spending on cancer is directed to LMICs (Praeger et al., 2018). The burden of cancer globally is anticipated to increase, with projections showing that 28.4 million new cases of cancer will occur in 2040 (Sung et al., 2021). The coronavirus disease-2019 (COVID‑19) pandemic has impacted screening, detection, and treatment of cancer, potentially increasing the morbidity and mortality associated with cancer for years to come (Cancino et al., 2020).
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.132 | 0.045 |
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".