Bibliographic record
Abstract
Slow but steady progress has been made in the earlier diagnosis and better treatment of gynecological cancers, particularly over the last 50 years. Cervical cytology screening programs, where implemented, have led to a remarkable reduction in both the incidence and mortality from clinically invasive cervical cancer. This relatively simple technology has been truly one of the major success stories of modern medicine, but unfortunately this technique has not been uniformly applied to all women in the world, particularly to women in developing countries. New research into cervical cancer etiology, the role of HPV, and the development of vaccines against this virus offer a great hope particularly for developing countries. In addition, the combination of radiotherapy and chemotherapy has resulted in a marked improvement in outcome results for women with advanced cervical cancer. Ovarian cancer has seen the development of effective chemotherapy strategies for this disease. Currently this disease remains one of the major scourges in industrialized countries but the continued evolution of knowledge with regard to optimum sequencing of chemotherapeutic agents and surgery offers the prospect for better outcomes, less morbidity and a better quality of life. Ongoing research into the development of newer chemotherapeutic agents and a better understanding of the actual mechanisms regarding the efficacy of chemotherapy and drug resistance offers great promise for the future. Endoscopic surgery for staging and also for therapy shows promise for improved quality of life as well as outcomes for patients in the future and offers the challenge of trying to make this technology readily available to all women in the world. As we gain a better understanding of the molecular basis of disease and health we will truly be able to intervene in a preventive mode in the new millennium.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".