Bibliographic record
Abstract
Cervical cancer is the second most common cancer in women worldwide, with more than half a million new cases diagnosed in 2005.1 The disease disproportionately affects the poorest regions—more than 80% of cases are found in developing nations, mainly in Latin America, sub-Saharan Africa, and the Indian subcontinent.1 Cervical cancer is an important cause of early loss of life as it affects relatively young women. Important advances have taken place in the diagnosis and treatment of this cancer in recent years. Surgery or chemoradiotherapy can cure 80-95% of women with early stage disease (stages I and II) and 60% with stage III disease (table⇓).2 3 4 5 View this table: International Federation of Gynaecology and Obstetrics (FIGO) staging classification (FIGO 1995, Montreal): cervical carcinoma #### Summary points We searched the literature to identify all relevant articles published from 1966 to March 2007 (PubMed and Cochrane database) using a combination of the terms “cervical cancer”, “diagnosis”, and “management”. Variables of interest were cervical cancer, surgery, chemotherapy, radiotherapy, chemoradiotherapy, complications of treatment, recurrence, and follow-up. Much of the clinical management discussed in this review was based on meta-analyses, systematic reviews, and phase III randomised controlled trials (RCTs). Infection with high risk types of human papillomavirus is the main cause of cervical cancer.6 This has obvious implications for primary prevention (vaccination) and secondary …
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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".