Overview of major salivary gland cancer surgery in Ontario (2003–2010)
Bibliographic record
Abstract
BACKGROUND: The primary objective of this study is to describe variations in incidence rates, resection rates, and types of surgical ablations performed on patients diagnosed with major salivary gland cancers in Ontario. METHODS: All major salivary gland cancer cases in Ontario (2003-2010) were identified from the Ontario Cancer Registry (n = 1,241). Variations in incidence rates, resection rates, and type of surgical therapy were compared by sex, age group, neighbourhood income, community population, health region, and physician specialty. RESULTS: Eight-year incidence rates per 100,000 vary significantly by sex (male: 15.5, female: 9.7), age (18-54 years: 6.7, 75+ years: 53.4), neighborhood income (lowest quintile: 11.8, highest quintile: 13.7), and community size (cities with a population greater than 1.5 million: 10.6, cities with a population of less than 100,000: 14.7). There was a significant correlation between the likelihood to receive a resection and age with the elderly (75+ years) being the least likely to receive resection (69%). Large differences in incidence and resection rates were observed by health region. Otolaryngology-Head & Neck surgeons provide the majority of total/radical resections (95%). CONCLUSIONS: Major salivary gland cancer incidence rates vary by sex, age, neighborhood income, community size, and health region. Resection rates vary by age and health region. These disparities warrant further evaluation. Otolaryngology-Head & Neck Surgeons provide the majority of major salivary gland cancer surgical care.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".