Technological advances in radiotherapy for cervical cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: To discuss the important technological advances that have taken place in the planning and delivery of both external beam radiotherapy and brachytherapy for patients with locally advanced cervical cancer, and the implications for improved clinical outcomes. RECENT FINDINGS: Technological advances in external beam radiation treatment and brachytherapy for patients with cervical cancer allow more precise targeting of tumour and relative sparing of surrounding normal organs and tissues. Early evidence is emerging to indicate that these advances will translate into improvements in tumour control and reduced side effects. However, there are patient, tumour and treatment-related factors that can detract from these benefits. Foremost among these is complex, unpredictable and sometimes dramatic internal tumour and normal organ motion during treatment. The focus of current research and clinical development is on tracking internal anatomic change in individual patients and adapting treatment plans as required to assure that optimal tumour coverage and normal tissue sparing is maintained at all times. The success of this approach will depend on clear definitions of target volumes, high resolution daily soft tissue imaging, and new software tools for rapid contouring, treatment planning and quality assurance. SUMMARY: Radiation treatment of locally advanced cervical cancer is evolving rapidly, driven by advances in technology, towards more individualized patient care that has the potential to substantially improve clinical outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".