A cluster randomized, controlled trial of breast and cervix cancer screening in Mumbai, India: methodology and interim results after three rounds of screening
Bibliographic record
Abstract
Cervix and Breast cancers are the most common cancers among women worldwide and extract a large toll in developing countries. In May 1998, supported by a grant from the NCI (US), the Tata Memorial Hospital, Mumbai, India, started a cluster-randomized, controlled, screening-trial for cervix and breast cancer using trained primary health workers to provide health-education, visual-inspection of cervix (with 4% acetic acid-VIA) and clinical breast examination (CBE) in the screening arm, and only health education in the control arm. Four rounds of screening at 2-year intervals will be followed by 8 years of monitoring for incidence and mortality from cervix and breast cancers. The methodology and interim results after three rounds of screening are presented here. Good randomization was achieved between the screening (n = 75360) and control arms (n = 76178). In the screening arm we see: High screening participation rates; Low attrition; Good compliance to diagnostic confirmation; Significant downstaging; Excellent treatment completion rate; Improving case fatality ratios. The ever-screened and never-screened participants in the screening arm show significant differences with reference to the variables religion, language, age, education, occupation, income and health-seeking behavior for gynecological and breast-related complaints. During the same period, in the control arm we see excellent participation rate for health education; Low attrition and a good number of symptomatic referrals for both cervix and breast.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".