Trends in the epidemiology of head and neck cancer in London
Bibliographic record
Abstract
BACKGROUND: Head and neck cancers [HNCs] are biologically heterogeneous tumours. The objectives of this study were to describe trends in incidence of HNCs amongst London residents by sex, age, anatomical site, deprivation and ethnicity. METHODS: Annual age-standardised incidence rates [ASRs] were calculated on HNC registration data, overall and for specific cancer sites, by sex and morphology (1985-2010) and area-based socio-economic deprivation score (2006-2010). Age-standardised incidence rate ratios [IRRs] for the main ethnic groups were calculated by cancer site, using White males and females as the reference groups (1998-2009). RESULTS: The ASR of HNC in males increased by 40% from 17.3 [95% CI: 15.8-18.6] to 24.2 [95% CI: 22.5-25.8] per 100 000 and in females by 87% from 7.0 [95% CI: 6.2-7.8] to 13.1 [95% CI: 11.9-14.2] per 100 000. Seventy-three per cent of cases spanned four cancer sites: larynx, thyroid, oral and oropharynx. Larynx was most common (23%), and had the highest male: female ratio (6 : 1); ASRs decreased significantly over time, most notably in males [P < 0.001]. Oral cavity was the second most common (21%), with a male: female ratio of 2 : 1, and increasing ASRs in both sexes [P < 0.001]. The majority of cases were male (64%) and from deprived areas (59%). Deprivation was associated with a significantly higher incidence for larynx (males), oropharynx (males and females) and oral cavity (females) [P < 0.05]. The age-specific rate for middle-aged adults (45-64 years) was high for oropharyngeal cancer. The incidence of thyroid cancers increased significantly in both sexes [P < 0.001], and this was the only site more common in females. One in five cases with known ethnicity was from a non-White group (20%). Compared with their White counterparts, Bangladeshi females had a higher incidence of oral, laryngeal and thyroid cancers; Chinese males and females had a higher incidence of nasopharyngeal cancer; and Pakistani and Indian females and Indian males also had higher incidence of oral cancer. CONCLUSIONS: HNCs are increasing in London males and females with significant variation by cancer site over time; oral and oropharyngeal cancers show the most significant rise, with implications for public health action and service provision.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".