The Geography of Primary Hepatic Neoplasms Treatments in Canada: Changes in Latitudes and Changes in Attitudes
Bibliographic record
Abstract
BACKGROUND: Studies on treatment modalities for primary hepatic neoplasms (PHN) in Canada are lacking. Our primary aim was to analyze the age-standardized incidence of hepatic resection, ablation, transplantation, and embolization for PHN between 2002 and 2013. Secondary aim was to evaluate temporal trends for these treatment modalities. STUDY DESIGN: National Canadian Cancer Registries were accessed for relevant epidemiological data. Age-standardized incidence of treatment ratios (SIRs) was calculated and comparisons were performed for Atlantic Canada, Ontario, the Prairies, and British Columbia. RESULTS: British Columbia recorded the highest SIRs for ablation (1.9; 95% CI 1.8-2.0), hepatic resection (1.2; 95% CI 1.1-1.3), and transarterial locoregional therapies (2.8; 95% CI 2.4-3.2). For hepatic resection, the lowest SIR was found in Atlantic Canada (0.7; 95% CI 0.6-0.9), while the Prairies recorded the lowest estimate for transarterial therapies (0.2; 95% CI 0.1-0.4). Liver transplantation had the highest SIR in Ontario (1.5; 95% CI 1.3-1.6) and the lowest SIR in British Columbia. No significant temporal changes in SIRs were observed for any of the treatments except for transarterial therapies. CONCLUSIONS: Treatment of PHN in Canada differs by geography. Variations might be due to differences in expertise or access to therapeutic modalities.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".