Ethnic disparity in cervical cancer stage at diagnosis: A retrospective study in an Israeli referral‐center
Bibliographic record
Abstract
OBJECTIVE: To compare stage and survival of cervical cancer between Jewish and Arab women in a tertiary medical center in Israel. METHODS: Retrospective study of consecutive women diagnosed with cervical cancer in a single institution between 2010 and 2021. We compared Jewish and Arab patients using univariate, multivariable, and survival curves analysis. RESULTS: Overall, 207 Jewish women and 45 Arab women were included with a median follow up of 20 months (interquartile range 7-46 months). The groups did not differ in median body mass index, mean age at diagnosis, or menopausal status. Arab women had higher parity. Arab women were at a higher risk to be diagnosed with advanced stage disease (≥2b) (84.4% vs. 57% Arab and Jewish women, respectively, P < 0.001). In a multivariable regression analysis, Arab descent was found to be the only independent factor associated with advanced stage disease (odds ratio 3.95, 95% confidence interval 1.54-10.10). Overall survival and stage-specific survival were not different between the ethnic groups. CONCLUSIONS: Advanced stage at diagnosis was more prevalent in Arab women compared with Jewish women with cervical cancer, whereas stage-specific survival was similar. Further studies addressing possible contributing factors to inequality should be undertaken to find corrective measures.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".