Does a diagnosis of GIST predict a second primary cancer?
Bibliographic record
Abstract
10537 Background: There have been reports of a high frequency of metachronous cancers in patients diagnosed with GIST. The purpose of this study was to identify and describe patients with GIST who develop second primary cancers, and to calculate standardized incidence ratios (SIRs) to quantify the risk of additional malignancy. Methods: This was a retrospective, population-based cohort study using SEER data. Individuals diagnosed with GIST from 2001-2009 were identified as having a malignant primary tumor in the digestive tract, and included the following sites: C15.0-C26.9; C48.0-48.8; C49.4-49.5; C80.9 and a recorded histology code of 8935 and 8936. This restricted timeframe was imposed to account for changes in the recording of GIST incidence. Individuals with a previous cancer diagnosis or diagnosed post mortem only were excluded. Multiple primary SIRs and 95% confidence intervals (CI) were calculated using SEER*Stat software (V.7.1.0) and compared to general population rates. The SIR was interpreted as an estimate of relative risk (RR). Comparison of characteristics between single and multiple cancer GIST patients was performed using chi-square tests, p-values of <0.05 were considered significant. Results: We identified n=1397 cases of GIST, of which 1291 analyzed. We observed a statistically significant increased incidence of second tumours in patients with a primary GIST (n=78, RR 1.36, 95% CI:1.1-1.7). Older age (p<0.001) and tumor grade (p=0.014) were associated with second primaries, with grade being the only significant variable remaining after logistic regression. In both sexes we observed a significantly increased incidence of kidney cancer (RR 4.3, 95% CI: 1.7-8.9). In females there was a 3 fold higher incidence of colon cancer (RR 2.96 95% CI: 1.2-6.1). Conclusions: Patients with a diagnosis of GIST have a higher incidence of second cancers when compared with standardized incidence in the general population. High grade GISTs were associated with an additional malignancy. Both sexes were observed to have increased incidence of kidney cancer, with females at an increased risk of developing colon cancer. As part of GIST surveillance, screening for colon cancer in females and kidney cancer in both sexes may be considered.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".