The management of vaginal bleeding in advanced cervical cancer
Bibliographic record
Abstract
Cervical cancer is the fourth most diagnosed malignancy worldwide.This narrative review aimed to assess various treatment methods for reducing or completely stopping vaginal bleeding, which is one of the most common and alarming complaints reported by women with cervical cancer.The treatment of bleeding depends on its intensity, potential causes (reversible and irreversible), stage of the disease, and prognosis.In advanced-stage patients receiving palliative care, interventional methods such as embolization or surgical ligation of internal iliac vessels, or epigastric or uterine arteries are usually not possible due to the patient's poor general condition.The treatment of choice is conservative therapy, which includes the performance of vaginal tamponade (usually using sterile gauze, haemostatic gauze, or cellulose sponges), the use of anti-haemorrhagic drugs (tranexamic acid, vitamin K), and, in selected clinical situations, palliative radiotherapy.The literature on the subject points to reports on the effectiveness of topical 4% formalin solution, Mohs paste, Monsel's solution, thrombin, or epinephrine in inhibiting vaginal bleeding.However, these are not supported by data from randomized clinical trials.Data available in databases on the alleviation of cervical vaginal bleeding are limited, so there is a need for further clinical research on this topic.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".