Risk of invasive cervical cancer after three consecutive negative Pap smears
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.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".