Bibliographic record
Abstract
The benefit of radiation is immense in the field of gastroenterology. Radiation is used daily in different gastrointestinal imaging and diagnostic and therapeutic interventional procedures. Radiotherapy is one of the primary modalities of treatment of gastrointestinal malignancies. There are various modalities of radiotherapy. Radiotherapy can injure malignant cells by directly damaging DNA, RNA, proteins, and lipids and indirectly by forming free radicals. External beam radiation, internal beam radiation and radio-isotope therapy are the major ways of delivering radiation to the malignant tissue. Radiation can also cause inflammation, fibrosis, organ dysfunction, and malignancy. Patients with repeated exposure to radiation for diagnostic imaging and therapeutic procedures are at slightly increased risk of malignancy. Gastrointestinal endoscopists performing fluoroscopy-guided procedures are also at increased risk of malignancy and cataract formation. The radiological protection society recommends certain preventive and protective measures to avoid side effects of radiation. Gastrointestinal complications related to radiation therapy for oncologic processes, and exposure risks for patients and health care providers involved in diagnostic or therapeutic imaging will be discussed in this review.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".