Innovations in the treatment of invasive cervical cancer
Bibliographic record
Abstract
Invasive cervical cancer is characterized by basement membrane-invading lesions capable of metastasizing through the lymphatic and vascular systems. Treatment methods were reviewed by panelists at the Second International Conference on Cervical Cancer (Houston, TX, April 11-14, 2002), and new opportunities for translational research were discussed. Reviews encompassed hysterectomy with or without lymph node dissection or cervical conization in cases with microinvasion and radical trachelectomy with or without lymph node dissection as fertility-sparing surgery. Chemoradiation is used to treat advanced cervical malignancies, and the risks and benefits of radiotherapy are significant. Pelvic exenteration is used to treat certain types of recurrences. Use of the Miami pouch for continent urinary diversion was highlighted. Gynecologic oncologists expect novel in vivo imaging techniques currently being developed to help guide therapy choices within the next decade. The most significant research priorities are large group-randomized trials involving fertility-sparing procedures and the management of microinvasive carcinoma (MICA); better identification of candidates for chemoradiation; and the development of innovative approaches to exenteration. Improving diagnostic technologies, refining the criteria by which therapies are chosen, and preserving fertility remain challenges in selecting the most appropriate treatment for invasive cervical cancer. Research advances in both diagnosis and treatment are expected to improve therapy and 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".