Epidemiology and survival of neuroendocrine tumors in Ontario: A 15-year population-based study.
Bibliographic record
Abstract
184 Background: A recent study of the SEER database in the United States showed a 5-fold increase in neuroendocrine tumours (NETs) over the last 30 years. An increasing incidence has also been reported in Norway, Sweden, England, Holland, Italy and Japan, but interestingly not in Denmark and Switzerland. The objective of our study is to describe the incidence, anatomical distribution and survival of NETs in Ontario. Methods: A population based study was initiated using the Ontario Cancer Registry, cross-linked with the Registered Persons Database and the Canadian Institute of Health Information Discharge Abstract Database. All cases of NETs were identified in Ontario (> 13 million persons) from 1994 to 2009. Baseline demographic, clinical and outcomes data were abstracted to allow for an analysis of annual incidence rates, and overall survival. Results: A total of N = 5619 cases were identified. The incidence rate increased from 2.46/ 100,000 (95% CI, 2.13-2.83) in 1994 to 5.86/ 100,000 (95% CI, 5.40 – 6.35) in 2009. The median age was 62 with 50.5% female cases. When divided by site, bronchopulmonary NETs where the most common (22%), then jejunum/ileum (17%) and rectal (16%) NETs, while pancreatic NETs (pNETs) and gastric NETs were 10% and 5% respectively. The absolute increase in the study period was most pronounced for pNETs (6-fold), rectal (5-fold) and gastric (5-fold) NETs. Metastatic disease was documented in 45% of the cases; 20% at diagnosis and 25% during follow-up. The 5-and 10-year overall survival (OS) was 61% and 46% respectively, for the entire population. Site specific 5-year OS were: rectal (87.0%), small bowel (73.4%), gastric (67.4%), colon (64.3%) and pancreas (48.8%). 5-year OS was compared for patients with and without metastatic disease after diagnosis (69.0% vs 40.1%, p<0.0001). Conclusions: There appears to be a significant increase of reported cases of NETs in Ontario, Canada, particularly pNETs, rectum NETs and gastric NETs. This supports much of the population-based reports worldwide. Survival appears to vary significantly according to anatomical site and extent of disease. Further research is required to understand the impact of this cancer previously perceived to be rare but clearly increasing.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".