The role of intensity modulated radiotherapy in gynecological radiotherapy: Present and future
Bibliographic record
Abstract
AIM: This manuscript reviews the English language literature on the use of intensity modulated radiation therapy (IMRT) for gynecologic malignancies, focusing on the treatment cervical cancer. BACKGROUND: Radiation therapy plays a key role in both definitive and adjuvant treatment of these patients, although efforts continue to minimize acute and chronic toxicity. IMRT is an attractive option because of the potential to dose escalate to the target while sparing organs at risk. METHODS AND MATERIALS: The English language literature was reviewed for relevant studies. RESULTS: Multiple heterogeneous studies have showed dosimetric and clinical benefits with reduction in acute and late gastrointestinal, genitourinary and hematologic toxicity, especially in the post hysterectomy scenario and for dose escalation to para-aortic nodes. Consensus is evolving regarding necessary margins and target delineation in the context of organ movement and tumor shrinkage during the course of radiotherapy. Protocols with daily soft-tissue visualization are being investigated. CONCLUSIONS: Consistency in approach and reporting are vital in order to acquire the data to justify the considerable increased expense of IMRT.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".