Gastrointestinal Stromal Tumours: A 10 Year Multicenter Audit
Bibliographic record
Abstract
Background: Gastrointestinal stromal tumours (GISTs) are unique neoplasms of the gastrointestinal (GI) tract. The development of targeted therapeutic agents such as imatinib mesylate (Glivec) has altered the way on how we now manage these rare malignancies. The aim of this study was to evaluate the management of GISTs in three Irish tertiary hospitals. Methods: We performed a retrospective, multicenter audit of patients diagnosed with gastrointestinal stromal tumours over a ten year period (2005-2015). Results: 110 patients were included in the study. Abdominal pain was the most common presenting symptom, reported in 30% of patients, while 31% were incidental findings. The stomach was the most common primary site of disease, observed in 77% of cases. 15 patients had metastatic disease at the time of diagnosis (14%), and 10 of these patients had liver involvement. More than half of patients (61%) were managed with surgical excision alone (61%), while 24 were managed with surveillance and 28 patients treated with adjuvant Glivec, which was generally well tolerated. 18 patients (20%) demonstrated recurrent or progressive disease after first line treatment. 102 patients (93%) are alive today. Conclusion: While surgery is widely regarded as the primary treatment modality for GISTs the addition of imatinib mesylate has enabled physicians to deliver more personalised treatment while optimising patient outcomes.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".