Associations between small intestine cancer and other primary cancers: An international population‐based study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cancer of the small intestine is a rare neoplasm, and its etiology remains poorly understood. Analysis of other primary cancers in individuals with small intestine cancer may help elucidate the causes of this neoplasm and the underlying mechanisms. We included 10,946 cases of first primary small intestine cancer from 13 cancer registries in a pooled analysis. The observed numbers of 44 types of second primary cancer were compared to the expected numbers derived from the age-, gender- and calendar period-specific cancer incidence rates in each registry. We also calculated the standardized incidence ratios (SIR) for small intestine cancer as a second primary after other cancers. There was a 68% overall increase in the risk of a new primary cancer after small intestine carcinoma (SIR = 1.68, 95% confidence interval [CI] = 1.47-1.71), that remained constant over time. The overall SIR was 1.18 (95% CI = 1.05-1.32) after carcinoid, 1.29 (1.01-1.63) after sarcoma, and 1.27 (0.78-1.94) after lymphoma. Significant (p < 0.05) increases were observed for cancers of the oropharynx, colon, rectum, ampulla of Vater, pancreas, corpus uteri, ovary, prostate, kidney, thyroid gland, skin and soft tissue sarcomas. Small intestine cancer as a second primary was increased significantly after all these cancers, except after oropharyngeal and kidney cancers. Although some of the excess may be attributable to overdiagnosis, it is plausible that most additional cases of second primary cancers were clinically relevant and were due to common genetic (e.g., defects in mismatch or other DNA repair pathways) and environmental (e.g., dietary) factors.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it