Current Concept of Bacterial Vaginosis in Cervical Cancer
Bibliographic record
Abstract
Invasive cervical cancer is often associated with bacterial vaginosis (BV) caused by both non-pathogenic and pathogenic bacteria and other microorganisms. BV goes un-noticed during the clinical course of cervical cancer. The incidence of BV is very high among women from developing countries with poor genital hygiene. Maintainence of normal vaginal ecosystem in healthy vagina is through control of optimal ratio between non-pathogenic and pathogenic organisms. Cervical cancer causes disruption of the normal vaginal and cervical mucosa leading to alteration of ratio between commensals and pathogenic organisms. The natural vaginal microflora is being dominated by lactobacilus species compared to pathogenic aerobic and anaerobic microorganisms. Disruption of vaginal microenvironment leads to change in vaginal flora and associated inflammation. There are very limited literature available on the exact incidence of BV in cervical cancer patients. Increased nitrosamine content in BV could possibly progress to higher DNA damage, change in cytokine profile thus compromise immune defence against human papilloma virus infection. Both radiotherapy and chemotherapy are potential immunosuppressive agent thus could facilitate spread of endogenous bacteria to manifest as poor outcome to treatment secondary to tissue hypoxia and hypoperfusion. This article is a review of current literature associated with BV in cervical cancer. J Clin Gynecol Obstet. 2014;3(1):1-7 doi: http://dx.doi.org/10.14740/jcgo175w
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".