Pattern of use of PET/CT scanning in gastrointestinal cancers in one cancer center in Saudi Arabia.
Bibliographic record
Abstract
e17529 Background: PET/CT scan is useful in certain clinical indications. However, because of high cost and maintenance demand, its routine use is not recommended. Moreover, PET scanning in many non-indicated conditions can be useless or even confusing. The National comprehensive cancer network (NCCN) has set up evidence based guidelines for the use of PET/CT. We retrospectively evaluated the local value and pattern of PET/CT scan use in non-colorectal GI cancers in our hospital, and whether it follows the evidence based guidelines or not and whether it’s use actually improves the patient’s outcome or not. Methods: Patients, who were diagnosed with non-colorectal GI cancers during 24 months (2007-2008) at our hospital, were included in this study if they had one or more PET/CT scans. Questions were answered as to whether PET/CT changed the stage or affected the management in each case. The indications of use as well as quality of the scan report were outlined. Results: Results: 77 patients with 107 PET/CT scans were included. Median age: 59 (21-86). Males: 58.8%36. Diagnosis: 46.8% esophageal cancer and GEJ cancer, 15.6% gastric, 11.7% pancreatic, 11.7% heptobiliary, 10.4% neuroendocrine tumors, 2.6 % GIST, 1.3% small bowel cancer. Indications of the PET/CT: staging in 59.8%, follow up after finishing treatment in 14.9%, restaging at relapse in 8.4%, assessing response after/during treatment in 3.7%, FU of previous PET/CT in 12.1% and others in 0.9%. PET/CT changed the stage in 19.6%, and affected the management plan in 11.2% only. The PET/CT for the lesions that could have changed the stage reported indeterminate result in 29.9% of cases. Pathological pursuit of the PET/CT result for lesions that were indeterminate and could have changed the stage was done in only 23.1% of cases. Conclusions: PET/CT scan use in our hospital does not follow an evidence based approach. Overuse was documented. Therefore, local guidelines of use are suggested.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".