Global cervical cancer outcomes and national cancer system characteristics.
Bibliographic record
Abstract
5535 Background: Significant global health disparities persist in cervical cancer, with over 85% of cases and deaths occurring in low- and middle-income countries (LMICs). In many settings, access to screening, vaccination, and treatment is limited. Despite being largely preventable through HPV vaccination and early detection, many women around the world still face inadequate healthcare infrastructure, lack of awareness, cultural stigma, and gender barriers to seeking care. Therefore, we evaluated global health system metrics that may inform efforts to improve equity in access to cervical cancer care globally. Methods: Estimates of age-standardized mortality-to-incidence ratios (MIR) were derived from GLOBOCAN 2022 for female patients of all ages with cervical cancer. We collected health spending as a percent of gross domestic product, physicians/1000 population, nurses and midwives/1000 population, surgical workforce/1000 population, GDP per capita, Universal Health Coverage Service Coverage Index (UHC index), availability of pathology services, human development index (HDI), gender inequality index (a combined metric of health, empowerment, and economic agency), radiotherapy centers/1000 population, and out-of-pocket expenditure as percentage of current health expenditure. We evaluated the association between MIR and each metric using univariable linear regressions. Metrics with p<0.0045 (Bonferroni corrected) were included in multivariable models. Variation inflation factor (VIF) allowed exclusion of variables with significant multicollinearity. R2 defined goodness of fit. Results: On univariable analysis, all 11 metrics were significantly associated with MIR of cervical cancer (<0.001 for all). After including metrics that were significant on univariable analysis, HDI demonstrated significant collinearity (VIF=19). Therefore, after correcting for multicollinearity, the final multivariable model with 10 variables had R2 of 0.79. On multivariable analysis, the following variables were independently associated with lower (improved) MIR for cervical cancer: 1) nurses/midwives per 1000 population (β=–0.0071, p=0.029) and 2) UHC index (β=–0.0023, P=0.013). In addition, greater gender inequality was associated with greater (worse) MIR (β=0.30, P=0.002). Conclusions: This global analysis of health-system metrics suggests promoting progress towards UHC and strengthening the nursing/midwifery workforce may be independently associated with improved cervical cancer mortality-to-incidence ratio. Furthermore, greater gender inequality was associated with worse MIR. These findings may inform further efforts to improve global cervical cancer care and underscore the importance of gender equity in improving global cancer outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".