Do Cervical Cancer Patients Diagnosed with Opportunistic Screening Live Longer? An Arkhangelsk Cancer Registry Study
Bibliographic record
Abstract
The aim of the current study was to compare cervical cancer (СС) patients diagnosed with and without screening in terms of: (i) sociodemographic and clinical characteristics; (ii) factors associated with survival; and (iii), and levels of risk. A registry-based study was conducted using data from the Arkhangelsk Cancer Registry. It included women with newly diagnosed malignant neoplasm of the uterine cervix during the period of 1 January 2005 to 11 November 2016 (N = 1548). The Kaplan-Meier method, the log-rank test, and Cox regression were applied. Most participants who were diagnosed by screening were at stage I and died less frequently from CC than those diagnosed without screening. The latter group was also diagnosed with СС at a younger age and died younger. Younger individuals and urban residents diagnosed with stage I and II, squamous cell carcinoma had longer survival times. Cox regression modeling indicated that the hazard ratio for death among women with CC diagnosed without screening was 1.61 (unadjusted) and 1.37 (adjusted). CC diagnosed by screening, cancer stage, patient residence, histological tumor type, and age at diagnosis were independent prognostic variables of longer survival time with CC. Diagnosis of CC made within a screening program improved survival.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".