A study of multi-beam PDT treatment in cervical cancer caused by various HPV genotype infections
Bibliographic record
Abstract
Cervical cancer, associated with persistent human papillomavirus (HPV) infections, continues to be common among women of reproductive age. With the development of HPV molecular diagnostic techniques, a growing number of people are tested positive for various high-risk HPV genotypes, the causative agent of cervical cancer. Consequently, there is a need to develop treatment methods for such potential cervical cancer patients. Standard treatments consist of excisional methods which do not target HPV infections and have been reported to increase the risk of reproductive problems. Previous studies have shown reduced HPV levels after photodynamic therapy (PDT) and its efficacy against cervical cancer. The coordinated application of light and photosensitizer is needed to limit cell death to the lesion and preserve the surrounding healthy tissue. However, localizing light exposure can be difficult, especially in minimally accessible areas such as the cervix. To address this, we investigated the in vitro and in vivo effect of a multi-beam PDT system, in which the light beams can be individually adjusted to match the lesion’s size and shape. The findings suggest that this multi-beam PDT system has the potential of becoming a more conservative fertility-sparing option that can localize treatment and meet patients’ reproductive needs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".