Survival of Patients With Cervical Cancer in Rural India
Bibliographic record
Abstract
Background: Patients’ survival after diagnosis of cervical cancer is indirectly influenced by socio-economic factors. We evaluated this survival and its socio-economic determinants in a rural population in south India. Methods: We assessed 165 women diagnosed with cervical cancer from the routine care control arm of a randomized screening trial conducted in rural south India. Kaplan-Meier curves were plotted to illustrate the observed survival of cancer patients. The effect of socio-economic factors was assessed using Cox proportional hazards regression analysis. Results: The 5-year observed survival was 32.5%, ranging from 9% for stage IV to 78% for stage I cancers. Women with poor socio-economic status (SES) had up to a 70% higher risk of death. Higher household income was significantly associated with poorer survival. However, most women in the higher income group were married women and housewives, hence with no personal income. Conclusion: Cervical cancer survival was disappointingly low in these rural populations of India and stage of disease at diagnosis was the strongest determinant. A higher household income is not always associated with women being empowered in terms of seeking healthcare. The study findings further stress the importance of strengthening prevention and screening opportunities to women in rural populations. J Clin Gynecol Obstet. 2015;4(4):290-296 doi: http://dx.doi.org/10.14740/jcgo367w
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".