The use of precision radiotherapy for the management of cancer-related pain in the abdomen
Bibliographic record
Abstract
PURPOSE OF REVIEW: Abdominal pain due to cancer is a significant and debilitating symptom for cancer patients, which is commonly undertreated. Radiotherapy (RT) for the management of abdominal cancer pain is underused, with limited awareness of its benefit. This review presents a discussion on current precision RT options for the management of cancer pain in the abdomen. RECENT FINDINGS: Precision RT focuses on delivering targeted and effective radiation doses while minimizing damage to surrounding healthy tissues. In patients with primary or secondary liver cancer, RT has been shown to significantly improve liver related cancer pain in the majority of patients. Also, symptom sequelae of tumour thrombus may be relieved with the use of palliative RT. Similarly, single dose, high precision stereotactic RT to the celiac plexus has been shown to significantly improve pain in patients with pancreatic cancer. Pain response for adrenal metastases has been less commonly investigated, but small series suggest that stereotactic body RT may reduce or alleviate pain. SUMMARY: RT is an effective option for the treatment of abdominal cancer pain. RT should be considered within the multidisciplinary treatment armamentarium, and may be successfully integrated, alone or in conjunction with other treatment modalities, in abdominal cancer-related pain.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.005 | 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".