Risk of invasive cervical cancer after pap smears: the protective effect of multiple negatives
Bibliographic record
Abstract
OBJECTIVE: To determine the relationship between the number of initial negative Pap smears and risk of subsequent cervical cancer. DESIGN: A cohort study was conducted using data from the British Columbia Cervical Cancer Screening Program and British Columbia Cancer Registry. The analysis used a random sample (1%) of women aged 20-69 with Pap smears and all cases of invasive cervical cancer diagnosed between 1994 and 1999. Each negative screen defined the beginning of a screening interval and intervals longer than five years were truncated. The following variables were created for each interval: age at the beginning of the interval, interval length, previous cytological abnormality, previous cervical procedure and number of preceding consecutive negative screens. The relationship between these variables and risk of squamous cervical cancer was determined using survival analysis methods. RESULTS: A total of 388 cases of invasive cervical cancer (252 squamous) were included in the study from a study population of over 3.3 million Pap smears. The risk of invasive squamous cancer increased with time since the last negative screen, history of cytological abnormality and history of cervical therapeutic procedure. Risk was not significantly related to age (P=0.2) but was highest in women aged 30-49. Multiple consecutive negative pap smears were associated with reduced risk in women with a history of moderate atypia (P<0.0001), but not in women without a history (P=0.4). CONCLUSIONS: Multiple consecutive negative cytology was not associated with reduced risk of invasive cervical cancer in women with no history of cytological abnormality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".