Detection of Early Cancers by Quantitative Cytology
Bibliographic record
Abstract
Abstract It has long been recognized that detecting cancers in their early, non-invasive stage is the best strategy to control malignant diseases. This has been best demonstrated by the example of cancer of the uterine cervix. Before screening for early signs of this malignancy, the prevalence of invasive cancer of the uterine cervix in the developed world was as high as 30 women per 100,000 and the mortality was 12-15 per 100,000 women per year, (all figures represent age standardized data). Since the introduction of cervical screening programs by Pap smears, the incidence of invasive cervical cancer and mortality due to this cancer has fallen dramatically. In British Columbia, for example, where population screening was introduced 50 years ago, the incidence and mortality have decreased several-fold and are at present below 6 and 3 per 100,000, respectively (1,2). It is believed that these figures could be even lower by encouraging more women into the program and by improving both sensitivity and specificity of the cytology.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".