Focused ultrasound and concurrent chemotherapy for the treatment of advanced pancreatic cancer: A systematic review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The combination of focused ultrasound (FUS) and chemotherapy is a novel treatment for pancreatic cancer. This paper reviews the literature on this combined therapy. METHODS: The medical literature was searched according to PRISMA guidelines. Inclusion criteria were any study of patients with pancreatic cancer undergoing treatment with FUS and chemotherapy. Data extracted included stage, radiologic response, resection rate, survival, and adverse events. RESULTS: The initial search yielded 212 citations; 10 studies met inclusion criteria (9 retrospective cohorts; 1 randomized trial). A total of 631 patients received FUS + chemotherapy; 63.6% being stage 4, and 29.7% stage 3. Patient selection, FUS parameters, and chemotherapy used were all heterogeneous. Overall survival ranged from 7.4 to 21.6 months, radiologic response rate was 44.1%, and 24.4% of stage 3 patients underwent resection. All four studies with a comparison group demonstrated improved survival. FUS + chemotherapy decreased pain in 69.7% of patients. Severe adverse events occurred in 0.65%. CONCLUSIONS: The literature on combined FUS and chemotherapy for pancreatic cancer is heterogeneous. There is good evidence that adverse events are low, and that it provides effective palliation. There is evidence to suggest oncologic benefit, however, this is subject to selection bias and prospective trials are needed.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".