Survival of head and neck cancer in Greenland
Bibliographic record
Abstract
OBJECTIVES: Head and neck cancer is frequent in the Inuit population of Greenland and is characterized by a very high incidence of Epstein-Barr virus associated nasopharyngeal carcinoma (NPC). However, information on the treatment and survival of Inuit head and neck cancer patients is practically non-existent. The aim of this study, therefore, was to analyse the epidemiological pattern, time course and survival of head and neck cancer patients in Greenland. STUDY DESIGN: Retrospective register-based study. METHODS: The Danish Civil Registration System, the Danish Cancer Registry and hospital-based registries were used to identify all patients resident in Greenland diagnosed with head and neck cancer during the period 1994-2003. Data were analysed with regard to clinical characteristics, treatment delay and survival. RESULTS: A total of 125 patients were identified. The age-standardized incidence rate for all head and neck cancer cases was 28/100,000 for males and 19/100,000 for females. High incidence rates were found for NPC and oral cancers. Of all cancers, 47% were stage IV at the time of diagnosis, while 61% of all NPC's were stage IV. The median delay from date of first symptom to treatment was 248 days for all cancers. The overall crude 5-year survival rate for all sites together was 35% and for NPC 20%. CONCLUSION: Survival of head and neck cancer in Greenland is very low. Delays in treatment and inadequate follow-up on treatment complications are probable causes. The improvements in treatment for NPC and other head and neck cancer cases over the last decades are yet to be seen in this Inuit population.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".