Invasive cervical cancer: a failure of screening
Bibliographic record
Abstract
BACKGROUND: Cervical screening is an effective prevention measure. It is unclear whether cervical cancer results from non-participation in screening or from failures in detection by screening. Analysis of the screening history of patients with cervix cancer may contribute to understanding failures in prevention. METHODS: A cohort of patients presenting during 1 year was identified. Dates and results of cervical smears in the 4 years prior to presentation were extracted from a screening database. Patients were grouped as follows: 'No screening'--no Pap records; 'Pre-diagnostic'--one or more Pap tests within 6 months of presentation; 'Sporadic screening'--one Pap test between 6 and 48 months prior to presentation; and 'Regular screening'--at least two Pap tests 6-48 months before presentation. RESULTS: 225 patients were identified (median age: 48 years, range 25-107). Eighty- eight had no records of screening; a further 66 were categorized as pre-diagnostic. These two groups (68% of incident cases) were considered not to have participated in routine screening. A further 15% had sporadic screening tests, but only 37 patients (16%) had evidence of regular screening. Clinically, 53, 41 and 6% presented with early, locally advanced and metastatic disease, respectively. Older patients (>50 years) were more likely to present with advanced disease (61 vs 37% at least Stage II). CONCLUSIONS: These results suggest that the failure to prevent invasive cervix cancer in this population can largely be attributed to failures in recruitment for screening.
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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".