An Audit of the Cervical Cancer Screening Histories of 246 Women With Carcinoma
Bibliographic record
Abstract
OBJECTIVE: Women with cervical carcinoma and residing in the Calgary Health Region between 1996 and 2001 were audited to characterize factors in the opportunistic cervical cancer screening pathway contributing to screening failures. MATERIALS AND METHODS: The cohort consisted of 246 women. Information on their Pap tests and colposcopic/gynecologic examinations was obtained from the files of Calgary Laboratory Services and their colposcopic/cancer center treatment charts. Screening failure factors were defined, and frequencies were calculated. RESULTS: Screening failure factors were as follows: (1) 41 (16.7%) were not screened, that is, no Pap test screening; (2) 29 (11.8%) were underscreened, that is, no Pap test within 12 months of diagnosis; (3) 28 (13.7%) were undersampled, that is, the Pap test result was negative; (4) 34 (13.8%) had no referral for a colposcopy/gynecology examination, and/or it was delayed for more than 3 months; (5) 18 (13.2%) had delayed referral for examination of an atypical glandular cell-high-grade squamous intraepithelial lesion and higher Pap test for more than 3 months; and (6) 73 (55.3%) were underdiagnosed, that is, the diagnosis in colposcopy examination was less than malignant. Underreported Pap tests and delayed Pap test reporting could not be fully investigated, but limited evidence suggested that underreporting contributed to some failures. CONCLUSIONS: Factors other than recruitment to cytological screening need targeted improvement if the region's cervical cancer prevention program is to be more effective.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".