Bibliographic record
Abstract
HEALTH ISSUE: Cervical cancer is one of the most common malignant diseases of women; it is diagnosed in almost half a million women every year and half as many die from it annually. In Canada and other industrialized countries, its incidence has decreased due to cytology screening. However, invasive cases still occur, particularly among immigrant groups and native Canadian women. Although incidence of squamous cell carcinomas has decreased, the proportion of adenocarcinomas has increased because Pap cytology is ineffective to detect these lesions. KEY FINDINGS: In Canada, cervical cancer will cause an estimated 11,000 person-years of life lost. In most Canadian provinces, early detection is dependent on opportunistic screening. Primary prevention can be achieved through health education (sexual behavior modification) and vaccination to prevent infection from Human Papillomavirus (HPV). The initial results from vaccination trials are encouraging but wide scale use is more than a decade away. DATA GAPS AND RECOMMENDATIONS: Most cases of cervical cancer occur because the Pap smear was either false negative, was not done or not done often enough. Appropriate recommendations and guidelines exist on implementation of cytology-based programs. However, most Canadian women do not have access to organized screening. Further research is needed to 1) evaluate automated cytology systems; 2) define appropriate management of precursor lesions and 3) deliver definitive evidence of HPV testing efficacy in long-term follow-up studies with invasive cancer as an outcome and 4) provide Canadian data to justify augmenting or modifying current programs to use HPV testing in secondary triage of equivocal Pap smears.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.048 | 0.011 |
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".