Small bowel cancers: A population-based analysis of epidemiology, treatment and outcomes in Ontario, Canada from 2005-2020
Bibliographic record
Abstract
Introduction Small bowel cancers are uncommon malignancies comprised of several histologies with variable treatments and prognoses. The current study describes the epidemiology, treatment, and outcomes of a large, population-based cohort of patients with small bowel cancers. Methods We performed a retrospective cohort study using linked administrative healthcare data from Ontario, Canada. Patients diagnosed with a small bowel cancer between 2005–2020 were included. Trends in incidence, treatments, and survival were explored by histology (adenocarcinoma, neuroendocrine tumors (NET), gastrointestinal stromal tumors (GIST), and lymphoma). Results A total of 5306 patients with small bowel cancers were identified. The most common histologies were NET (40.5 %) and adenocarcinoma (31.6 %). Over the study period the annual incidence of small bowel cancers increased from 1.54 to 2.78 per 100 000 and the likelihoods of receiving surgery and systemic therapy within one year of diagnosis both increased for all histologic subtypes except lymphoma. Median overall survival from diagnosis was 1.0 year for adenocarcinoma, 13.2 years for NET, 14.2 years for GIST, and 10.1 years for lymphoma. There was no trend towards improved median survival for adenocarcinoma by year of diagnosis; 0.94 years (2005–2010), 1.07 years (2011–2015), and 0.98 years (2016–2020). Discussion Small bowel cancers are increasing in incidence, with increasing use of surgery and systemic therapies. While survival is favourable for many small bowel cancers, it remains poor for adenocarcinoma. Better availability of cancer stage data and detailed histopathology within the database would facilitate future research. Synopsis This study of small bowel cancer in Ontario from 2005 to 2020 demonstrated increasing incidence, use of surgery, and systemic treatments. The most common histologies are neuroendocrine tumors and adenocarcinoma, with median survivals of 13.2 and 1.0 years respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".