Risk of invasive cervical cancer after three consecutive negative Pap smears
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: To determine the factors that influence risk of cervical cancer after three consecutive negative Pap smears. METHODS: A cohort study was conducted using data from the British Columbia Cervical Cancer Screening Program and British Columbia Cancer Registry. Analysis was based on a one percent sample of women aged 20-69 years with Pap smears enriched with all invasive cervical cancer cases diagnosed between 1994-99. Screening intervals, after three negative screens, were created with the following variables: age at beginning of interval, interval length, previous cytologic abnormality and previous cervical procedure. The risk of cervical cancer by histologic type was calculated using survival analysis methods. RESULTS: The sample consisted of 10,509 women, who contributed 28,309 intervals, and 371 cervical cancer cases. The incidence rate of invasive squamous cervical cancer increased with time since last screen up to six years. Women with a history of dysplasia remained at elevated risk for squamous cancer, hazard ratio=2.6 (95% confidence interval [CI]=1.9, 3.4) but age or previous procedure were not related to risk. No relationship between time since last screen and non-squamous cancer risk was found although history of a previous procedure was significant. The marginal effectiveness of Pap smears declined with increasing frequency of use. CONCLUSIONS: This study confirmed the preventive effect of Pap smear screening and its dependency on frequency of use. Women with a history of dysplasia, prior to three consecutive negatives, were at increased risk of developing invasive squamous cervical cancer compared with women with no such history.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.000 |
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 it