Is there a role for tirapazamine in the treatment of cervical cancer?
Bibliographic record
Abstract
INTRODUCTION: Cervical cancer is the second-most common malignancy in women worldwide. Cisplatin was introduced as a radiosensitizer in 1999 to improve chances of survival. Tumor cell hypoxia, however, remains a major limiting factor in the treatment of solid tumors with chemotherapy and radiation. There has since been significant interest in the use of bioreductive agents to overcome the hypoxia and improve survival. The addition of tirapazamine (TPZ) to conventional chemoradiation protocols in the management of cervical cancer held promise in the initial Phase I and II clinical trials in delaying recurrence and improving survival. However, GOG recently announced early closure of the Phase III trial of tirapazamine in cervical cancer due to a lack of increased survival. AREAS COVERED: This article covers the definition of hypoxic tumor cells, the markers of tumor hypoxia, methods for measuring hypoxia as well as the pharmacologic action of tirapazamine in hypoxic media. Furthermore, it critically evaluates TPZ's role in cervical cancer treatment and the drawbacks to the GOG study. The authors review all clinical trials published to date with special emphasis on cervical cancer. A systematic review of the literature was also undertaken with PubMed and Ovid. EXPERT OPINION: Despite the promising results from early clinical trials, it has been shown that the addition of tirapazamine appears to confer no benefits on progression-free or overall survival in patients with cervical cancer. Success in the future will require smaller randomized trials with biologic targets that have acceptable toxicity and efficacy.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".